Modeling on Social Health Literacy Level Prediction

Abstract

Nowadays, the health level of residents has become the focus of people’s attention. Under the background of the development of health service from “disease-centered” to “health-centered,” it is very important to improve the level of urban health and clarify the factors affecting urban health. Therefore, this paper quantifies the relationship between residents’ health literacy level and environment, average life expectancy, infectious disease mortality, and other indicators by selecting appropriate indicators and establishing a mathematical model. Based on the reciprocal linear combination of the collected index data and the corresponding health level value, the prediction model of social health literacy level (SPM) was established, and the qualitative prediction and quantitative analysis of citizens’ health literacy level were studied in depth. Based on the SPM model, we can roughly predict the level of health literacy in a region only based on the main variables identified in this paper. The consistency of the experiment shows that the model is effective and robust, and it reveals that environmental factors are the most important factors affecting residents’ health literacy level. The actual data show that THE SPM model is a timely and reasonable framework to measure the health literacy level of residents.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

Health literacy refers to the ability of individuals to acquire and understand health information and to use it to maintain and promote their own health, including basic knowledge and ideas, healthy lifestyle and behavior, and basic skills. It is an evaluation index that comprehensively reflects the development of national health undertakings [1, 2].

Health literacy is not an isolated concept. Health literacy is related to education [3–7], age [8], race [9], air pollution [10], chronic diseases [11, 12], social medical system [13], etc. However, due to the lack of objective indicators to reflect the overall health literacy level of residents in a certain region or country, the relationship between these indicators and the health literacy level of residents has not been well understood, which is often controversial. Therefore, it is not only of great significance in scientific research, but also of great value in helping people live a healthy life to quantify various indicators and establish a quantitative model to evaluate the health literacy level of residents in a region or country.

With the development of society, people pay more and more attention to the level of residents’ health literacy. For the tool to measure health literacy [14], some use experimental methods to evaluate health literacy level [15], some use Delphi survey technology [16], some use HLS-EU-Q questionnaire to measure health literacy level [17, 18], some use mixed multicriteria decision-making (MCDM) method to establish evaluation model [19], and some use dynamic factor model (DFM) [20].

Taking Shenzhen as an example, this paper discusses the relationship between indicators and residents’ health literacy level from the perspective of data analysis. By visiting government websites such as Health and Family Planning Commission, Statistical Bureau, Baidu, Souhu, Xinlang, and other search engines and news websites, this paper explores the relationship between health literacy and its multiple variables and makes a quantitative study of their correlation.

In order to explore the influence of many variables on health literacy, we selected 12 factors related to the level of health literacy in combination with 66 articles of health literacy of Chinese citizens. Then the main variables are obtained by fitting and screening. Using search engine results or data on social media as proxy shows that the main variables do greatly affect the real value of health literacy. The root of this phenomenon lies in three aspects: influencing citizens’ basic knowledge and concept, healthy lifestyle and behavior, and basic skills.

Several factors were selected to study the time series of health literacy, and the relationship between the indicators and the level of health literacy was explored. The results showed that PM2.5 (microgram cubic meters), health expenditure accounted for local financial expenditure, infectious disease mortality, and average life expectancy had the highest goodness of fit.

The paper is divided into six parts: the first part is the introduction. The second part includes data source, determination of variables, main variables, and time interval. In the third part, we studied four main variables related to health literacy: PM2.5 (microgram cubic meters), health expenditure, infectious disease mortality, and life expectancy. Then, in the fourth part, we constructed a health literacy level prediction model based on fitting results. The fifth part examines the accuracy and stability of the prediction model. Finally, the conclusions and discussions are presented in Part VI.

2. Data

Thirteen sets of data collected from the National Health and Family Planning Commission and the Local Health and Family Planning Commission from 2000 to 2017 were used for research in Shenzhen and the whole country. We will analyze the indicators affecting residents’ health prediction level through the trend and fitting of each variable in the past 18 years.

2.1. Data Sources

The National Health and Family Planning Commission (NHPC) includes the real values of national health level and the annual changes of various factors over the years. The real health level of local cities can be collected from the local health and family planning committee. Baidu, 360, and other large engines provide all kinds of news reports on health literacy level. At the same time, CCTV, Sohu, and Sina also broadcast in real time. In addition, local TV stations review the course of health literacy in the light of their own characteristics and development trends, in order to continuously improve and look forward to the future.

2.2. Definition of Variables

Through a thorough understanding of Article 66 of Chinese Citizens’ Health Literacy, combining basic knowledge and concept, healthy lifestyle and behavior, and basic skills, we have established 13 research directions of residents’ health literacy S(t), maternal mortality P(t), the ratio of household registration to nonhousehold registration H(t), the number of college graduates A(t), the number of hospitals N(t), the drinking water standard rate $Wt$, the number of beds B(t) per 1,000 population, the number of people participating in fitness activities E(t), infant mortality I(t), PM2.5 M(t), health expenditure F(t), infectious disease mortality $Gt$, and life expectancy L(t). Twelve basic variables except S(t) were established.

By fitting the basic variables and comparing the trend charts of each factor and health literacy level from 2000 to 2017, four variables with higher fitting degree are obtained, including PM2.5 M(t), health expenditure accounting for local financial expenditure F(t), infectious disease mortality $Gt$, and life expectancy L(t).

2.3. Time Interval ∆t

The true value of health literacy has been calculated since 2000. Every year, the relevant departments will test the level of citizens health literacy nationwide and locally, with an interval of one year. Therefore, we choose one year as our time interval (∆t), which is consistent with the frequency of the government updating the true value and ranking of health literacy.

3. Relevant Variables

3.1. Variable Screening

In order to find out the optimal model which can predict residents’ health literacy level, we first considered the 12 factors of maternal mortality P(t), percentage of household registration and nonhousehold registration H(t), number of college graduates A(t), number of hospitals N(t), drinking water compliance rate $Wt$, number of beds per 1,000 people B(t), number of participants in fitness (more than 1000) E(t), infant mortality rate I(t), PM2.5 content M(t), health expenditure accounts for local financial expenditure F(t), infectious disease mortality rate $Gt$, and average life span L(t).

In this paper, 12 considered variables were fitted with the health literacy level of residents, and the results can be seen in Figure 1. We found that the optimal model was fitted by four variables: PM2.5 content M(t), health expenditure accounts for local financial expenditure F(t), infectious disease mortality rate $Gt$, and average life span L(t).

[figure omitted; refer to PDF]

Figure 2 shows the development trend of the national residents’ health literacy level C(t) and Shenzhen residents’ health literacy level S(t) from 2000 to 2017. We found that the health literacy level of residents in Shenzhen and the whole country is in a growth trend. In 2016, two solid lines intersect. Figure 2 shows that the national residents’ health literacy level C(t) is higher than the Shenzhen residents’ health literacy level S(t) from 2000 to 2015, and the Shenzhen residents’ health literacy level S(t) is higher than the national residents’ health literacy level C(t) from 2016 to 2017 (see the yellow line section). The intersection point corresponds to the Shenzhen Municipal Government’s vigorous development of people’s livelihood in 2016, which is “to improve the quality of people’s livelihood and enhance the level of people’s livelihood security.” The document “The implementation opinions of Shenzhen Municipal Government on deepening the reform of medical and health system and building a strong city of health” was issued, along with the reform of medicine and health to establish a higher quality medical and health service system. In 2016, the Shenzhen government also issued “The Shenzhen Solid Waste Pollution Prevention and Control Action Plan” to promote environmental protection and ecological civilization construction in Shenzhen. Therefore, the results show that the residents’ health literacy level is closely related to healthcare and ecological environment.

3.3. The Infectious Disease Mortality Rate $Gt$

The infectious disease mortality rate $Gt$ is an index reflecting the severity of life-threatening diseases, indicating the frequency of death due to a disease in a certain period of time. The incidence of infectious diseases is an indicator of the severity of life-threatening diseases. Influencing factors of reducing mortality rate of infectious diseases in a certain area include improvement of sanitary conditions, improvement of water sources, education (especially women’s education), provision of medical services and infrastructure construction. These factors can also obviously affect people’s physical and mental health. Therefore, it is reasonable to take the mortality rate of infectious diseases as one of the factors to measure the health literacy level of residents in a certain area. Figure 3 shows the trend of infectious disease mortality and residents’ health literacy. As shown in the figure, the lower the mortality rate of infectious diseases, the higher the level of health literacy in the region. When $Gt>0.4$, the data points are more concentrated and fit well with the fitting line. When $Gt<0.4$, the distribution of data points is scattered and the fitting degree is not as good as before. This may be because with the improvement of medical and health conditions, the mortality rate of infectious diseases is decreasing dramatically and tends to be stable, so the influence of the mortality rate of infectious diseases on the health literacy level of residents is weakening. $R^2=0.333$. Therefore, we can choose the mortality rate of infectious diseases as an index to measure the health literacy level of residents.

[figure omitted; refer to PDF]

The fitting line $R^2$ is 0.323 based on scatter plot. We choose life expectancy as one of the indicators to measure the health literacy level of residents in a certain area.

4. Health Literacy Level Model of Residents

The fourth part shows that the content of PM2.5 $Mt$, the proportion of health expenditure in local financial expenditure $Ft$, the mortality rate of infectious diseases $Gt$, and the average life span $Lt$ all affect the health literacy level of residents. However, a linear fit $yt=Axt+C$ of each individual indicator measure to the observed residents’ health literacy level results in $R^2<0.3$, except for $1/Mt$, where $R^2=0.361$. Therefore, there is no single indicator that can adequately measure the health literacy level of residents. This indicates that it is necessary to measure the health literacy level of residents through the combination of indicators (see Appendix for the correlation of variables).

We therefore explored the predictive power of the sum of these variables with multipliers obtained via an ordinary least squares $\text{OLS}$ fitting process, resulting in $R^2=0.78$; the $p$ values of F-test for all variables are shown in Table 1. The $p$ values of variables $1/Gt$ and $1/Ft$ were greater than 0.05, so the two variables did not pass the test.

Table 1

$p$ values and β-coefficients.

	1/M(t)	1/$Gt$	1/F(t)	L(t)
$p$ value	≤0.001	0.179	0.423	0.03
β-coefficients	0.639	0.183	−0.13	0.621

We found that the four indexes multiplied and then fitted variables can pass the test and the prediction ability is not much different from the above model. Fitting formula$$\begin{array}{}\text{(1)}& \text{SPM}.S_{M}t=A\frac{Ft}{Mt}\frac{Lt}{Gt}+C.\end{array}$$

R-square is 0.640. The $p$ value of variable F-test is less than 0.001, which passes the test, so the $\text{SPM}$ model is accepted. That is to say, the SPM model is the best model in the models we tested.

According to model 1, we assessed that the impact of that indicator on the health literacy level of residents was greater. Standardized β-coefficient is the corresponding regression coefficient in the regression equation calculated after data standardization [21, 22]. The standardized β-coefficient eliminates the influence of the unit of dependent variable and independent variable [23, 24], and its absolute value can directly reflect the influence degree of independent variable on dependent variable. Table 1 show that the content of PM2.5 is the strongest factor affecting the health literacy level of residents.

5. Experiments

Several experiments were designed to verify the predictive ability of the model $\text{SPM}$:

  Firstly, we use the data of the first ten years as training data to get the model coefficients $A=0.329$ and $C=-0.516$. Then we use this model to predict the health literacy level of residents in the next seven years (Figure 7). We found that this model can accurately predict the health literacy level of residents. Figure 7(a) shows that we compare the predicted value with the real value and find that the scatter points are around the line y = x, which indicates that the predicted value $S_{M}t$ is closely related to the real value $St$. Figure 7(b) shows the difference between the observed and the real values. $\mathrm{\Delta }{S}_{M}t=St-S_{M}t$. The scatter points are distributed around the straight line on the day of X = 0 and the fluctuation is small, which indicates that the difference between the predicted value and the real value of the model is acceptable. Figure 7(c) is a comparison between the 95% prediction interval and the true value. We can see from the figure that most of the values are included in the prediction interval. There is a very low deviation in residents’ health literacy level because in some years, the official website of the government did not publish its exact value, so there may be permissible error in the value of residents’ health literacy level. Overall, Figure 7 shows that once the PM2.5 content M(t), health expenditure accounts for local financial expenditure F(t), infectious disease mortality rate $Gt$, and average life span L(t) are obtained in a certain area, the health literacy level of residents in this area can be predicted. Next, we will carry out further experiments.

  Next, we use the national data for further experimental proof. We use SPM model to predict the health literacy level of Chinese residents from 2000 to 2017. We can see from Figure 8(a) that the scatters of predicted and real values are distributed around the Y = X straight line, which shows that the two values are closely related. Figure 8(b) shows that the difference between the predicted value and the real value fluctuates at Y = 0, and the floating range is small, which further shows that the predicted value of this model has strong predictive ability. Figure 8(c) shows that the model is capable of capturing real values. Therefore, we can say that SPM model has a good predictive effect on residents’ health literacy level.

[figures omitted; refer to PDF]

[figures omitted; refer to PDF]

6. Discussion and Conclusions

In recent years, the National Health and Family Planning Commission has incorporated the evaluation index of residents’ health literacy into the national health development plan as an evaluation index to comprehensively reflect the development of national health. In this study, we try to quantify the relationship between residents’ health literacy and indicators and predict residents’ health literacy by indicators.

Because residents’ health literacy covers a wide range of areas, it is difficult to obtain it directly. So we selected 12 variables, and after screening, we got four indexes with the highest fitting degree—PM2.5 M(t), health expenditure accounted for local financial expenditure F(t), infectious disease mortality $Gt$, and life expectancy $Lt$. These four indicators can be measured separately. Therefore, this paper establishes a prediction model of residents’ health literacy (SPM) based on four indicators. We find that when we input the index data, the difference between the predicted value and the actual value produced by the model is very small, even neglected. For individual outlier data, we investigated the local information and found that the government website did not publish its exact value, so there may be permissible error in the value of their residents’ health literacy level.

Of course, with the rapid development of society, various health problems are constantly emerging. We still have a long way to go in the future on how to predict and control the response and how to find new indicators to measure the level of health literacy in order to continuously improve the model in this paper. At the same time, we hope that the SPM model in this paper can be easily extended to other areas where indicators are easy to measure. For example, health monitoring, land planning, and decision-making [25], asset accumulation and portfolio decisions in the financial sector [26, 27], natural gas demand forecasting [28], food safety, and other indicators are similar to health literacy tests. At the same time, it is hoped that this study can help improve the residents’ health literacy level, correctly grasp the basic knowledge and concepts, master healthy lifestyle and behavior, and learn the basic skills of health first aid.

Acknowledgments

This work was supported by the National Natural Science Foundation of China (nos. 71701115, 72171136), the Ministry of Education of Humanities and Social Science Project (no. 21C10445029), the National Social Science Foundation of China (no. 21BGL001), and Shandong Natural Science Foundation (ZR2020MG003).

Appendix

Appendix

Variable correlation

Table 2 shows the correlation results of four variables: PM2.5 content M(t), proportion of health expenditure in local financial expenditure F(t), mortality rate of infectious diseases G(t), and average life expectancy L(t). The results show that no single index can fully measure the level of health literacy of residents; it is necessary to combine indicators to measure the level of health literacy of residents.

Table 2

Variable correlation.

		L(t)	1/F(t)	1/G(t)	1/M(t)
L(t)	Pearson	1	0.721${}^{\ast \ast }$	0.299	−0.012
	Significance (bilateral)		0.001	0.228	0.961
	N	18	18	18	18
1/F(t)	Pearson	0.721${}^{\ast \ast }$	1	0.177	0.124
	Significance (bilateral)	0.001		0.482	0.623
	N	18	18	18	18
1/G(t)	Pearson	0.299	0.177	1	0.370
	Significance (bilateral)	0.228	0.482		0.130
	N	18	18	18	18
1/M(t)	Pearson	−0.012	0.124	0.370	1
	Significance (bilateral)	0.961	0.623	0.130
	N	18	18	18	18

${}^{\ast \ast }$Significant correlation was found at 0.01 level (bilateral).

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