Inequality in the hepatitis B awareness level in rural residents from 7 provinces in China

ABSTRACT

The hepatitis B (HB) awareness level is an important factor affecting the rates of HB virus vaccination. To better understand income-related inequalities in the HB awareness level, it is imperative to identify the sources of inequalities and assess the contribution rates of these influential factors. This study analyzed the unequal distribution of the HB awareness level and the contributions of various influential factors. We performed a cross-sectional household survey with questionnaire-based, face-to-face interviews in 7 Chinese provinces. Responses from 7271 respondents were used in this analysis. Multinomial logistic regression was used for the analysis of contributing factors, and the concentration index was used as a measure of HB awareness inequalities. The HB awareness level varied across participants with different characteristics. Multinomial logistic regression of the explanatory factors of the HB awareness level showed that several estimated coefficients and relative risk ratios were statistically significant for middle- and high-level awareness, except for sex, occupation, and household income. The concentration index of the HB knowledge score was 0.140, indicating inequality gradients disadvantageous to the poor. The contribution rate of socioeconomic factors was the largest (60.8%), followed by demographic characteristics (29.0%) and geographic factors (4.3%). Demographic, socioeconomic, and geographic factors are associated with the HB awareness inequality. Therefore, to reduce inequality, HB-related health education targeting individuals with low socioeconomic status should be performed. Less-developed provinces, especially with high proportions of poor residents, warrant particular attention. Our findings may be beneficial to improve the HB virus vaccination rate for individuals with low socioeconomic status.

KEYWORDS:

• awareness level
• China
• Hepatitis B
• inequality
• questionnaire
• vaccination

Introduction

Hepatitis B virus (HBV) infection is a significant public health problem worldwide; approximately 3 billion people have been exposed to HBV, and an estimated 2 billion people have been infected. Moreover, it is the leading cause of chronic hepatitis, cirrhosis, and hepatocellular carcinoma, and the 10th leading cause of death in the world.^1-3 An estimated 1 million people die each year from acute and chronic sequelae secondary to HBV infection.⁴ China has one of the highest carrier prevalence rates of HBV in the world, with HB affecting approximately 10% of the general population.⁵ It is estimated that HBV has chronically infected approximately 100 million Chinese people, making it the most prevalent life-threatening infection in China.⁶ It has also been estimated that the annual mortality related to acute and chronic HBV infections accounts for 263,000–300,000 deaths in China, which exceeds the combined annual mortality from tuberculosis, HIV/AIDS, and malaria.⁷

One of the most effective and feasible means to prevent HBV infection is by vaccination.⁸ The HBV vaccine is safe, highly immunogenic, and effective at preventing HB occurrence.⁹ One previous study reviewed the impact of HBV vaccination over the past 20 years in China, and found that the prevalence of HBV carriers in China was markedly reduced after the introduction of the universal HBV vaccination program.¹⁰ Therefore, the high rate of HBV infection can be attributed to low levels of HBV vaccination coverage. Interestingly, despite the implementation of a universal HBV vaccination program, the vaccination coverage remains low in most risk groups in most industrialized countries.¹¹ Approximately 20% of children under the age of 5 and 40% of children aged 5 to 19 years remain unprotected from HBV.⁷ Many studies have found that low parental, specifically maternal, literacy and knowledge regarding vaccines and the immunization schedule; poor socioeconomic status; and residence in rural areas are factors affecting the immunization completion rates and are associated with low immunization coverage.^12-20 Moreover, educational interventions have been demonstrated to increase health awareness; and knowledge about various diseases, and to successfully promote vaccine use and improve immunization coverage rates in both high-income and low-income countries.^21-23 Hence, improving HB knowledge is of importance for preventing HBV infection. Several studies have been performed on the level of HB awareness in different populations. Of note, one previous study suggested that many people were unvaccinated due to a lack of sufficient information about the HB severity and vaccine efficiency, and that many refused to vaccinate owing to fears of getting HB from the vaccination and due to potential side effects.^24,25 Therefore, low vaccination rates are to a large extent due to low levels of awareness about HB.

In addition, the level of HBV vaccination coverage may also be affected by economic factors. Existing research found that the household income significantly correlated with the HBV vaccination rate.²⁶ In fact, one previous study on HBV vaccination completion revealed differences in the immunization completion rates based on the participant socioeconomic status, despite free vaccinations being provided, with those with a higher household income being more likely to complete the vaccination series.²⁷ Hence, those with a lower household income may have relatively low vaccination rates, which results in health inequality. In other words, if the HB awareness level of the poor is lower than that of the rich, then the health disparities between the poor and rich will be greater. Therefore, it is imperative to study income-related inequalities in the HB awareness level and to identify the influential factors of such inequalities. However, to our knowledge, previous studies have mainly focused on knowledge assessment, influencing factors, and intervention measures,^28-34 while related studies on the differences in HB awareness level are lacking.

With this in mind, the main objective of the present study was to analyze the unequal distribution of the HB awareness level and the contribution of its affecting factors. We performed a cross-sectional survey among households in rural China, using questionnaire-based, face-to-face interviews. We analyzed the relationship between HB awareness and demographic, socioeconomic, and geographic determinants using descriptive statistics and multinomial logistic regression analysis. The inequality in HB awareness according to the household income level was analyzed by using the concentration index. To further determine the sources of inequality, the concentration index was decomposed into several determinants. Equity in the awareness level means that the health knowledge level in the population is evenly distributed, and that there are no socioeconomic and demographic differences. In contrast, inequity in the awareness level may lead to differences in the utilization efficiency of the HBV vaccine and in the infection risk in different populations, which may hinder health equity and social harmony.

Moreover, a few surveys of HB knowledge have indicated that sociodemographic factors, including the age group, educational level, employment status, marital status, and other factors such as household income, type of housing, and use of media for health education, seem to play important roles in the HB awareness level.^33,34 However, to date, attempts to quantify health disparities have only included demographic data (age, ethnicity, sex etc.), socioeconomic factors (income, education, occupation, marital status etc.), and geographic factors.^35-37 Taking into account the contributory factors of the HB awareness level, the present analysis draws on equality research on health and studied the effects of age, sex, education, marital status, occupation, income, and provinces on the inequity of the HB awareness level.

Finally, analysis of the distribution of health across social and economic groups has led to the development of measuring tools such as the concentration curve and concentration index.³⁸ A measure of inequality in health care using its concentration index with respect to individual income is usually decomposed into the sum of the different factors that have led to this inequality.³⁹ The present study incorporates the lessons learned from previous studies using the concentration index and linear decomposition method commonly used in the research on health equity as a means to study the HB awareness level of rural residents in China. This cannot only fully reflect the distribution of the factors associated with the HB awareness level in the general population but also determine the relative closeness between these factors and the inequity phenomenon, and help analyze the magnitude of the contribution rate of socioeconomic and demographic indicators.

The study aimed to measure the degree of HB awareness level distribution inequality among rural residents and to explore the significance of relative elements, thereby providing a scientific basis for designing public health service equalization strategies.

Results

Characteristics of the respondents

Table 1 presents the descriptive statistics for independent variables. From the table, we can see that the sex ratio difference was predominantly masculine; there were 41.8% males and58.2% females. Most respondents were aged 38–48 years, accounting for 23.9%. Most people were classified as having medium education (53.0%), followed by those with low education (44.8%), while highly educated people accounted for only 2.2%. Most people were married (87.8%), and farming was the main vocation (72.8%). Most participants resided in Hebei province (24.5%), followed by Jiangsu (19.0%) and Shandong (15.2%). Of all participants, 68.2%, 52.3%, and 50.4% were aware that the HBV vaccine works, that HB can spread between mother and infant, and that HB can spread through blood, respectively. Noteworthy, however, only 31.2% and 12.6% of the participants were aware that HB can spread through sex and that the HBV vaccine is valid for 6–10 years, respectively.

Table 1. Descriptive statistics for independent variables (N = 7271).

Variable	N	%
Male	3042	41.8
Female	4229	58.2
Age 18–28 years	1516	20.9
Age 28–38 years	1562	21.5
Age 38–48 years	1736	23.9
Age 48–58 years	1508	20.7
Age >58 years	949	13.1
Low education	3257	44.8
Medium education	3852	53.0
High education	162	2.2
Married	6382	87.8
Living alone	889	12.2
Farmer	5294	72.8
Worker	1639	22.5
Unemployed	338	4.7
Household income group 1	1454	20.0
Household income group 2	1454	20.0
Household income group 3	1454	20.0
Household income group 4	1454	20.0
Household income group 5	1455	20.0
Henan	954	13.1
Hebei	1779	24.5
Shandong	1108	15.2
Heilongjiang	443	6.1
Jiangsu	1382	19.0
Hainan	900	12.4
Ningxia	705	9.7
Are aware HB can spread between mother and infant	3800	52.3
Are aware HB can spread through blood	3666	50.4
Are aware HB can spread through sex	2269	31.2
Believe the HBV vaccine work	4960	68.2
Are aware the HB virus vaccine is valid for 6–10 years	913	12.6

Living alone includes single, widowed, and divorced individuals.

HB, Hepatitis B; HBV, Hepatitis B virus.

Hepatitis B awareness level of the participants

Table 2 shows the awareness score distributions of the participants. The median score was 2 (13.3%). Most participants scored 0 (23.0%), and only 3.6% scored at the highest level (score of 5), indicating that the awareness level of the rural population was very poor. According to their awareness score distributions, the participants were divided into 3 groups. Table 3 shows the group-based HB awareness level of the participants, with 38.9% and 25.2% of participants in the low and high level awareness groups, respectively.

Table 2. Hepatitis B awareness scores of the participants.

Score	n	%	Cumulative %
0	1673	23.0	23.0
1	1156	15.9	38.9
2	969	13.3	52.2
3	1643	22.6	74.8
4	1565	21.5	96.4
5	265	3.6	100.0
Total	7271	100.0	100.0

Table 3. Hepatitis B awareness levels of the participants.

Awareness level (score)	n	%	Cumulative %
Low level (score = 0 or 1)	2829	38.9	38.9
Middle level (score = 2 or 3)	2612	35.9	74.8
High level (score = 4 or 5)	1830	25.2	100.0
Total	7271	100.0	100.0

Table 4 shows the HB awareness levels of participants with different characteristics. The HB awareness level differed significantly according to sex (P = 0.01) and across the different age groups. Young people's awareness level was better than that of the elderly. Among the 3 youngest age groups there were relatively high proportions of people with a high awareness level; 509 (33.6%) in age group 18–28 years, 454 (29.1%) in age group 28–38 years, and 469 (27.0%) in age group 38–48 years. However, for the age groups of 48–58 and >58 years, only 283 (18.8%) and 115 (12.1%) people were classified into the high level awareness group. Moreover, the HB awareness level of people with a low level of education was lower than that of people with a high level of education (P < 0.001). The HB-related knowledge level of married people was better than that of people living alone (P < 0.001), and compared with farmers and the unemployed, workers' HB awareness level was higher (P < 0.001). Further, as the income increased, the HB awareness level of the population gradually increased. The HB awareness level also varied across provinces, with the lowest levels of HB awareness found in residents in Ningxia.

Table 4. Hepatitis B awareness levels of participants with different characteristics (N = 7271).

Variable	Low level, n (%)	Middle level, n (%)	High level, n (%)	P
Male	1222 (40.2)	1113 (36.6)	707 (23.2)	0.01
Female	1607 (38.0)	1499 (35.5)	1123 (26.6)
Age18–28 years	396 (26.1)	611 (40.3)	509 (33.6)	<0.001
Age 28–38 years	499 (32.0)	609 (39.0)	454 (29.1)
Age 38–48 years	635 (36.6)	632 (36.4)	469 (27.0)
Age 48–58 years	713 (47.3)	512 (34.0)	283 (18.8)
Age >58 years	586 (61.8)	248 (26.1)	115 (12.1)
Low education	1654 (50.8)	995 (30.6)	608 (18.7)	<0.001
Medium education	1144 (29.7)	1549 (40.2)	1159 (30.1)
High education	31 (19.1)	68 (42.0)	63 (38.9)
Married	2428 (38.0)	2320 (36.4)	1634 (25.6)	<0.001
Living alone	401 (45.1)	292 (32.9)	196 (22.1)
Farmer	2225 (42.0)	1818 (34.3)	1251(23.6)	<0.001
Worker	469 (28.6)	662 (40.4)	508 (31.0)
Unemployed	135 (39.9)	132 (39.1)	71 (21.0)
Household income group 1	704 (48.4)	457 (31.4)	293 (20.2)	<0.001
Household income group 2	609 (41.9)	496 (34.1)	349 (24.0)
Household income group 3	505 (34.7)	549 (37.8)	400 (27.5)
Household income group 4	482 (33.2)	556 (38.2)	416 (28.6)
Household income group 5	484 (33.3)	554 (38.1)	417 (28.7)
Henan	344 (36.1)	395 (41.4)	215 (22.5)	<0.001
Hebei	594 (33.4)	645 (36.3)	540 (30.4)
Shandong	260 (23.5)	487 (44.0)	361 (32.6)
Heilongjiang	210 (47.4)	129 (29.1)	104 (23.5)
Jiangsu	576 (41.7)	489 (35.4)	317 (22.9)
Hainan	445 (49.4)	268 (29.8)	187 (20.8)
Ningxia	400 (56.7)	199 (28.2)	106 (15.0)

P-values from chi-square tests for association.

Results of the multinomial logistic regression of the HB awareness level

Table 5 shows the results of the multinomial logistic regression of the HB awareness level against the explanatory factors. The results are reported for the middle and high levels of awareness, measured relative to the low level of awareness, which was used as the reference outcome. For the middle and high level awareness groups, several estimated coefficients and relative risk ratios were statistically significant, except for sex, occupation, and household income. With increasing age, the HB awareness level of residents was gradually reduced. On the other hand, a higher education level was associated with a higher awareness level, and the awareness level of the married population was higher than that of single people. Finally, regional differences were also important factors influencing the awareness level.

Table 5. Multinomial logistic regression of the hepatitis B awareness level against explanatory factors. The basis outcome is low level awareness. (N = 7271).

	Middle level awareness				High level awareness
	Coef.	SE	RRR	P	Coef.	SE	RRR	P
Female	−0.069	0.061	0.934	0.26	0.040	0.069	1.041	0.56
Age 18–28 years	1.260	0.117	3.526	<0.001	1.764	0.140	5.835	<0.001
Age 28–38 years	0.875	0.109	2.399	<0.001	1.254	0.134	3.503	<0.001
Age 38–48 years	0.584	0.105	1.792	<0.001	0.978	0.129	2.659	<0.001
Age 48–58 years	0.416	0.100	1.516	<0.001	0.567	0.129	1.763	<0.001
High education	0.897	0.237	2.452	<0.001	1.196	0.245	3.307	<0.001
Medium education	0.569	0.065	1.767	<0.001	0.655	0.074	1.926	<0.001
Married	0.181	0.092	1.198	0.05	0.230	0.106	1.259	0.03
Farmer	−0.216	0.137	0.806	0.11	0.028	0.165	1.028	0.86
Worker	−0.028	0.147	0.973	0.85	0.288	0.173	1.334	0.10
Household income group 1	−0.323	0.097	0.724	<0.001	−0.246	0.110	0.782	0.03
Household income group 2	−0.281	0.095	0.755	<0.001	−0.183	0.107	0.832	0.08
Household income group 3	−0.057	0.093	0.944	0.54	0.024	0.104	1.024	0.82
Household income group 4	0.028	0.092	1.029	0.76	0.124	0.101	1.132	0.22
Henan	0.924	0.123	2.519	<0.001	1.009	0.149	2.744	<0.001
Hebei	0.826	0.113	2.284	<0.001	1.311	0.135	3.709	<0.001
Shandong	1.508	0.125	4.516	<0.001	1.908	0.148	6.741	<0.001
Heilongjiang	0.112	0.152	1.118	0.46	0.572	0.174	1.771	<0.001
Jiangsu	0.573	0.125	1.773	<0.001	0.873	0.150	2.395	<0.001
Hainan	−0.115	0.125	0.891	0.35	0.103	0.149	1.108	0.49

Coef., coefficient; SE, standard error; RRR, relative risk ratio.

The reference outcome is low level awareness.

The concentration index decomposition results

Figure 1 shows that the concentration index is 0.140, calculated using equation (1(1) $C=\frac{2}{\mu }\mathrm{cov}(h,r)$(1) ), indicating that the distribution of the HB awareness level is unequal, which is disadvantageous to the poor. Here, we present the steps of the decomposition analysis to attempt to explain the sources of health inequalities.

In this subsection, we will illustrate the decomposition of the concentration index into its determinants. The total observed socioeconomic inequality in health can be translated into absolute contributions of the determinants. Computed using equation (3(3) $C={\sum }_k\left(\frac{{\beta }_k{\overline{x}}_k}{\mu }\right)C_k+\frac{G{C}_{ϵ}}{\mu },$(3) ), the contribution index for HB awareness scores is 0.140, indicating that the inequality gradients are disadvantageous to the poor. According to equation (3(3) $C={\sum }_k\left(\frac{{\beta }_k{\overline{x}}_k}{\mu }\right)C_k+\frac{G{C}_{ϵ}}{\mu },$(3) ), the absolute contribution is obtained by $\left(\frac{\beta \kappa \overline{\chi \kappa }}{\mu }\right)$ × $C_k$. To be specific, the absolute contribution of each determinant is calculated by its marginal effect by multiplying its mean and dividing by the mean of the health outcomes, followed by multiplying by the concentration index.

Figure 1. Concentration curve of participants' Hepatitis B awareness scores.

Table 6 shows the contributions of each determinant to the total inequality. In looking at demographic factors, the contribution to the total HB awareness inequality was 0.021, and the contribution rate was 29.0%. More specifically, the concentrations of females and the 18–48 year age groups were positive, while the concentration of those aged 48–58 years was negative. The contribution rate of the 18–48-year age group was the largest. Each age group contributed 0.011 (16.0%), 0.007 (8.0%), 0.006 (7.0%), and −0.003 (−2.0%), respectively, to the total explained inequality in the health outcome. Concerning socioeconomic determinants, initially, the contribution to the total HB awareness level inequality was 0.034, and the contribution rate was 60.8%. Specifically, education and marital status showed positive associations with the HB awareness level (positive marginal effect), and these people were all concentrated at the higher income level (positive concentration index), indicating that the degree of contribution to the inequality in health outcome was substantial, with those with high education, medium education, and married contributing 0.002 (5.6%), 0.011 (11.7%), and 0.001 (4.7%) to the inequality, respectively. Farmers and workers also showed positive contributions to the overall inequality. Moreover, the household income revealed intuitively positive associations with the health outcome and was disproportionately concentrated at the rich; that is, the contribution was in the forward direction to the total observed inequality. The contributions of income (28.1%) and education (17.3%) were the most significant socioeconomic determinants. As for geographical contributions, the marginal effects and contribution indices were all significantly positive for Hebei, Heilongjiang, and Jiangsu provinces. Residence in Henan and Shandong provinces showed large positive associations with the HB awareness level, whereas these rural dwellers were disproportionate in the lower income group; hence, their contribution was negative to the overall inequality. Residence in Hainan was negatively associated with HB awareness level; this factor also contributed to the inequality.

Table 6. The concentration index decomposition results of the hepatitis B awareness score.

Variables	CI	Marginal effect	Contribution	Contribution rates (%)
Demographic characteristics			0.021	29.0
Female	0.023	0.010	0.000	0.1
Age 18–28 years	0.105	1.094**	0.011	16.0
Age 28–38 years	0.086	0.8112**	0.007	8.0
Age 38–48 years	0.084	0.604**	0.006	7.0
Age 48–58 years	−0.078	0.374**	−0.003	−2.0
Socioeconomic factors			0.034	60.8
High education	0.304	0.710**	0.002	5.6
Medium education	0.099	0.441**	0.011	11.7
Married	0.014	0.181*	0.001	4.7
Farmer	−0.075	−0.014	0.000	4.3
Worker	0.242	0.136	0.003	6.5
Household income group 1	−0.625	−0.204	0.012	12.5
Household income group 2	−0.263	−0.134**	0.003	6.4
Household income group 3	0.035	0.003**	0.000	4.0
Household income group 4	0.287	0.063**	0.002	5.2
Geographic factors			0.006	4.3
Henan	−0.165	0.560**	−0.006	−4.0
Hebei	0.039	0.653**	0.003	2.1
Shandong	−0.020	1.012**	−0.001	−1.0
Heilongjiang	0.244	0.285*	0.002	1.4
Jiangsu	0.222	0.376**	0.007	5.3
Hainan	−0.127	−0.111	0.001	0.6
Total			0.061	94.1

The statistically significant estimates of marginal effects are highlighted (*p < 0.01, **p < 0.001).

Overall, the contribution rate of socioeconomic factors was the largest (60.8%), followed by demographic characteristics (29.0%). Geographic factors showed the lowest contributions to the overall HB awareness inequality (4.3%). Finally, it was also possible for us to compare the contributions of a variety of determinants to the inequality; for example, among the different socioeconomic characteristics, the contribution of income factor (28.1%) was larger than that of education (17.3%).

Discussion

It is clear that the HB awareness level is an important factor affecting the rates of HBV vaccination. However, the awareness level of the rural population is very poor, with 39.0% and 25.2% of participants in the low and high level awareness groups, respectively. Therefore, raising the HB awareness level is necessary and would be beneficial to improve the HBV vaccination rate for rural individuals.

Our findings indicate that there are income-related inequalities in the HB awareness level, which affect health equity. Our study analyzed the HB awareness inequality in China and also decomposed the equalities to reveal their determinants. The results showed that the concentration index of the HB knowledge score was 0.140, indicating that the inequality was concentrated among the poor, that is, a phenomenon of inequality existed.

This survey enabled us to identify areas of HB-related knowledge inequality, as well as the associations of demographic, socioeconomic, and geographic factors with the levels of HB knowledge inequality. Among the demographic factors, age 18–28 was the major contributor (Table 6). Compared to the other demographic factors, this variable had both a higher marginal effect and a higher concentration index. The latter implies that this group tended to be more concentrated at the higher income levels. Further, the contribution index indicated that older people tend to be distributed in poor areas with low economy. A previous survey of HBV infection and awareness found that the awareness level of the elderly was significantly lower than that of younger people.⁴⁴ The main reason for this finding is likely the low levels of concern about their health in the elderly, and difficulty in accepting new knowledge. In China, elderly tend to be more concerned about the health status of their children's families instead of their own; therefore, they do not actively seek ways to gain knowledge about HB. In addition, memory impairment and stubbornness are also main barriers to improve the knowledge about HB.

Beyond that, low socioeconomic status, including low education, was a main contributor to the inequality in the present study. Being in the medium and low education groups contributed strongly to the inequality in the HB awareness level. Comparing across the five income quintiles, a lower income level, particularly the lowest income group, was the major contributor to the health outcome inequality. The contribution rate was larger as the income decreased. In accordance with these findings, previous studies have confirmed that socioeconomic factors play an important role in interpreting health inequality; better knowledge is associated with higher household income and educational level.^45-47 Vaccination is an effective means of preventing HB. However, the cost of vaccination greatly reduces the vaccine service accessibility for populations at lower economic levels. Furthermore, most participants belonging to the low awareness level group are poor. Thus, the combination of poverty and low level of awareness may increase the risk of HBV infection. On the other hand, compared with people of low educational attainment, highly educated people not only have a strong ability of learning HB-related knowledge, but can fully recognize the importance of health. In turn, this leads to increased inequality. In addition, occupation and marital status are also important influencing factors. From the occupation perspective, the average economic level of workers is higher than that of farmers. Compared with working people, farmers' daily activities are fewer, and their contact with the outside world and access to HB prevention knowledge are limited. Moreover, the proportion of the farming population is very large in China. Therefore, HB knowledge popularization should focus on farmers.

Geographically, residing in Jiangsu province was the main contributor to inequality in the HB awareness level; the contribution rate was positive and large. Further, residence in Henan province showed a large negative contribution to the inequality in Hepatitis B awareness level. These results may reflect a lack of sound policies in these areas.

One of the main strengths of our study is the sampling methods used, including the stratification according to the level of economic development and distance to vaccination sites, which should help ensure the sample's representativeness. A second strength is that we used linear decomposition of the concentration index method in the analysis as a means to explain the sources of health inequalities. This approach is suited for measurements of inequalities in health; to obtain the contributions of various factors represented by demographic, socioeconomic, and geographic characteristics; and for eventually drawing a reliable conclusion. Moreover, our study has certain academic and policy implications. It is well known that one of the most effective and feasible means to prevent HBV infection is by vaccination. Furthermore, the HB awareness level is an important factor affecting the rates of HBV vaccination. However, there are income-related inequalities in the HB awareness level, which affect health equity. Our study identified the sources of these inequalities and assessed the contribution rates of the influential factors. Our findings may be beneficial to reduce the HB awareness level inequality and improve the HB vaccination rates for individuals with low socioeconomic status.

However, there are also some limitations to this study. First, this was a cross-sectional study. Hence, in future studies, longitudinal data are needed to better understand the categories of determinants and their link to the HB awareness level inequality. In addition, the decomposition approach is limited, as it only includes measured variables. Besides the demographic, socioeconomic, and geographic characteristics, other determinants such as cultural characteristics, health resources, and health education policies, may also contribute to inequalities.^48,49 Finally, the method of measuring socioeconomic status might not be accurate or appropriate, because we ignored the different weights for different characteristics of the population.

Conclusions

This paper used decomposition analysis to demonstrate that certain demographic, socioeconomic, and geographic characteristics particularly contribute to poor-rich differences in the HB awareness level. Being older and having lower education and income were the main contributors to the inequality in the HB awareness level. Geographically, residing in Jiangsu province was the major contributor.

HBV vaccination is one of the most effective and feasible means to prevent HBV infection. Moreover, the HB awareness level is an important factor affecting the rates of HBV vaccination. Therefore, reducing HB awareness level inequality is extremely important. Our decomposition results are similar to the findings of some previous studies on health inequality. The socioeconomic status is still the main influencing factor of inequality, and geographic factors are always of great concern in inequality research. In order to advance the equality in terms of access to health care, the relevant policies should pay more attention to economic development in underdeveloped regions, with increased health education and health investments, for the purpose of promoting health and for ensuring health equity for the whole population. Overall, our study will be beneficial to improve the HBV vaccination rates for individuals with low socioeconomic status.

Methods

Sources of data, health outcome variables, and their determinants

In this survey, the level of economic development and geographical and epidemiological characteristics were taken into comprehensive consideration. Sampling was conducted in the provinces of Henan, Hebei, Shandong, Heilongjiang, Jiangsu, Hainan, and Ningxia. Within each province, counties were stratified by the level of economic development (low, medium, high), and, within each county, villages were stratified by the travel distance to the nearest vaccination site (short, medium, long). On the village level, households were randomly selected (probability proportional to household size) in larger villages (>200 households), while in smaller villages, all households were included. We used questionnaire-based, face-to-face interviews to investigate the demographic characteristics, socioeconomic situation, and HB awareness levels of the residents. This survey included 7,271 valid households. Table 7 presents the basic information of the 7 sample provinces.

Table 7. Basic information of the 7 sample provinces.

Provinces	Number of investigations	Average annual income in the past 5 years (CNY)
Henan	705	16835.31
Hebei	902	25461.89
Shandong	1381	20975.22
Heilongjiang	444	29325.11
Jiangsu	1105	34876.38
Hainan	1781	20416.98
Ningxia	953	16219.72
Total	7271	24150.53

1 CNY = 0.1498 USD

All statistical analyses were performed using STATA version 12.0. We selected 5 questions about HB prognosis, transmission, and vaccine-related issues from the questionnaire to measure the awareness level of the respondents, and conducted score evaluation toward these 5 questions (score range, 0–5). The scores as dependent variables are presented in Table 8.

Table 8. Questions and scores regarding hepatitis prognosis, transmission, and vaccine-related issues.

Question	Score
Can hepatitis B spread between mother and infant?	yes = 1, otherwise = 0
Can hepatitis B spread through blood?	yes = 1, otherwise = 0
Can hepatitis B spread through sex?	yes = 1, otherwise = 0
Do you believe that the hepatitis B virus vaccine works?	yes = 1, otherwise = 0
Are you aware that the hepatitis B virus vaccine is valid for 6–10 years?	yes = 1, otherwise = 0

The dependent variable is equal to the sum of the scores of these 5 questions. The higher the score, the higher the hepatitis B awareness level is.

As determinants of the HB awareness level (logistic regression) and the inequality in the HB awareness levels (concentration index decomposition), we included demographic, socioeconomic, and geographical characteristics, as follows: i) The demographic characteristics consisted of sex and age groups (18–28, 28–38, 38–48, 48–58, and >58 years). Male sex and age >58 years were used as the reference groups. ii) The socioeconomic characteristics consisted of education (low, medium, high), marital status (married and living alone – the latter included single, widowed, and divorced individuals), occupation (farmers, non-agricultural occupation, unemployed), and income level. The income level was defined based on the quintiles in the distribution of household per capita income, so that the households in the lowest quintile were assigned to household income group 1, while households in the upper quintile were assigned to household income group 5. For the socioeconomic variables, the reference groups were low education, living alone, unemployed, and income group 5. iii) The geographic characteristics were represented by seven provinces located in the eastern (Hebei, Shandong, Jiangsu, Hainan), middle (Henan, Heilongjiang), and western regions of China (Ningxia). These provinces vary in several aspects, including economic development. The province-level average annual incomes in the past 5 years in Henan, Hebei, Shandong, Heilongjiang, Jiangsu, Hainan, and Ningxia were 16,835, 25,462, 20,975, 29,325, 34,876, 20,417, and 16,220 Yuan, respectively. Thus, Henan and Ningxia are considered relatively poor compared to the other provinces, while Jiangsu is the richest of the seven provinces. Ningxia was used as the reference group.

Measurement of socioeconomic status

We used household gross annual income per capita as the measure of socioeconomic status. The gross income (without deductions of costs) was reported as an average for the past 5 years, and divided by the number of household members.

Measurement of socioeconomic inequality in health: The concentration index

To date, six measures of inequality have been used in the literature on inequalities in health: the range, the Gini coefficient with the associated Lorenz curve, a pseudo-Gini coefficient with an associated pseudo-Lorenz curve, the index of dissimilarity, the slope index of inequality with the associated relative index of inequality, and the concentration index with the associated concentration curve.^40,41 However, only the slope index of inequality and the concentration index with the associated concentration curve are suited for the measurement of inequalities in health, because the concentration index reflects the experiences of the entire population and is sensitive to the distribution of the population across socioeconomic groups; moreover, it ranks individuals by socioeconomic status rather than by health, and ensures that the socioeconomic dimension to inequalities in health is taken into account.^40,41 In this study, we thus selected the concentration index to identify the inequality of the HB awareness level.

The concentration index can be calculated in various ways; one of the most commonly used computing formulas is as follows: ⁴²

(1) $C=\frac{2}{\mu }\mathrm{cov}(h,r)$(1) where $r$ is the rank depending on the living standards distribution from the lowest to the highest economic level; $h$ is the score of HB awareness level, which is measured as a health section variable; and $\mu$ is the mean of $h$.

The concentration index is commonly used to measure the magnitude of inequity in the distribution of health variables associated with the economic level. It ranges between −1 and 1. In this analysis, the concentration index was used to qualify the degree of socioeconomic-related inequality in the HB awareness level. If the concentration index equals zero, there is no socioeconomic-related inequality in the distribution of the health variable of interest. If the concentration index takes a positive value, the HB awareness inequality gradient is disadvantageous to the poor. Conversely, when it is negative, it is disadvantageous to the rich. A larger absolute value of the concentration index reflects more pronounced inequality. If the contribution of a determinant is positive, it is indicated that this determinant is a key factor in explaining the total inequality of the health outcome. However, if the concentration of a determinant is negative, the determinant might not be a significant factor or may reduce the overall inequality. The larger the absolute value of the contribution, the more substantial is its effect on the HB awareness level inequality. The corresponding contribution rates are obtained by each absolute contribution divided by the overall explained part of the contribution index.

Explaining the determinants of inequalities in health: Decomposition of the concentration index

To explain the sources of health inequalities, one previous study showed that the concentration index of the health variable of interest can be decomposed into the contributions of various factors represented by demographic, socioeconomic, and geographic characteristics, together with an unexplained residual component.³⁵

Thus, suppose we have a linear regression model linking our health variable of interest, $h$, to the intercept $\alpha$_, the relative contributions of a set of k determinants, x_k, and the residual error ϵi, are as follows: ^35,43(2) $h_i\hspace{0.17em}=\hspace{0.17em}\alpha +{\sum }_k{\beta }_k{x}_{ki}+{ϵ}_i.$(2) where ${\beta }_k$ is a coefficient and ϵ_i is an error term. We assume that the sample population in our study faces the same coefficient vector, ${\beta }_k$. Interpersonal variations in $h$ are assumed to derive from systematic variations across the income groups in the determinants of $h$, i.e., x_k Based on equation (2(2) $h_i\hspace{0.17em}=\hspace{0.17em}\alpha +{\sum }_k{\beta }_k{x}_{ki}+{ϵ}_i.$(2) ), given the relationship between$h$_i and x_ki, the concentration index can be decomposed as follows:(3) $C={\sum }_k\left(\frac{{\beta }_k{\overline{x}}_k}{\mu }\right)C_k+\frac{G{C}_{ϵ}}{\mu },$(3) where ${\overline{x}}_k$ is the mean of determinant k(x_k); $\mu$ is the mean of the health variable of interest ($h$); $C_k$ is the concentration index for determinant k(x_k); and GC_ϵ is a generalized concentration index for the error term ϵ_i, which is analogous to the Gini coefficient corresponding to the generalized Lorenz curve.

Vasoontara et al.³⁵ demonstrated that, in equation (3(3) $C={\sum }_k\left(\frac{{\beta }_k{\overline{x}}_k}{\mu }\right)C_k+\frac{G{C}_{ϵ}}{\mu },$(3) ), two components make up the overall inequality in health outcome – an “explained” component and an “unexplained” component. The “explained” or “deterministic” component equals the weighted sum of the concentration indices of the k repressors, where the weight for x_kis simply the elasticity of $h$ with respect to x_k (evaluated at the sample mean), while the GC_ϵ is treated as the “unexplained”or “residual” component, which reflects the inequality in health that cannot be explained by systematic variation in determinants across the different income groups. For the “explained” component, there are two main elements, $(\frac{\beta \kappa \overline{\chi \kappa }}{\mu })$ and $C_k$, in the decomposition process. $(\frac{\beta \kappa \overline{\chi \kappa }}{\mu })$ is defined as the impact each determinant has on the health outcome, and $C_k$is the magnitude of unequal distribution of each determinant depending on the living standards. Only when the contribution of a determinant is large and it is unequally distributed between people of lower and higher socioeconomic status, it can be considered as a key factor in explaining socioeconomic inequalities in health.³⁵ In our study, ${\beta }_k{\overline{x}}_k{C}_k/\mu$ is the contribution of x_k to the degree of inequality of the HB awareness level.

Ethics and institutional approval

Participation was voluntary and potentially sensitive questions were not included in the questionnaire. All study participants were informed that they could refuse to answer any question. The project was approved by the Medical Ethics Committee at the Shandong University School of Medicine (Grant No. 201001052). This study was partly funded by the Norwegian Research Council (Project no. 196400/S50).

Abbreviations

H	=	hepatitis B
HBV	=	hepatitis B virus

Disclosure of potential conflicts of interest

No potential conflicts of interest were disclosed.