CN110634541A - A kind of oral health data collection and analysis method - Google Patents

Abstract

The invention discloses a method for collecting and analyzing oral health data, including a data processing workstation for storing and statistically analyzing oral health epidemiological investigation data, a storage transfer station, and a tool for collecting oral health epidemiological investigation data. A data entry workstation, the data entry workstation is connected to the data processing workstation through a storage transfer station through a control signal, and the data entry workstation includes inspection questionnaires, financial management, leave management, track management, message management, log management, answered management, Manage offline. The present invention fills up the gap in this research field, not only can effectively manage and store on-site survey data, facilitate the promotion and application of medical institutions in the whole province and even the whole country, provide a unified and standardized data platform for future data mining, but also improve project management and provide Investigate the performance management and financial management of doctors and other workers to provide evidence support.

Description

A kind of oral health data collection and analysis method

technical field

The invention belongs to the field of oral cavity data collection and analysis, and in particular relates to a method for oral health data collection and analysis.

Background technique

In order to grasp the oral health status of Chinese residents, my country carried out four national oral health epidemiological surveys in 1983, 1995, 2005 and 2015, which provided a scientific basis for formulating oral health policies. However, in these four large-scale surveys, paper-based questionnaires and checklists were used, which required a second manual entry after the survey. Financial management, track management, etc. lacked systematic software management. A certain degree of difficulty has been added to the on-site investigation work and post-management.

Contents of the invention

In order to overcome the defects that the existing paper questionnaires and checklists require secondary manual entry and lack of uniformity in management, which is not convenient for later viewing, the present invention provides a method that can effectively obtain survey data, realize precise data analysis and management, and It can save the secondary manual entry, eliminate the secondary entry error, and greatly save the oral health data collection and analysis method of manpower, material resources and financial resources.

What the present invention adopts in order to realize the above object is:

A method for collecting and analyzing oral health data, including a data processing workstation for data storage and statistical analysis of oral health epidemiological investigations, a storage transfer station, and a data entry workstation for collecting oral health epidemiological investigation data, The data entry workstation is connected to the data processing workstation through a control signal through a storage transfer station, and the data entry workstation includes inspection questionnaires, financial management, leave management, track management, message management, log management, answered management, and offline management.

Further, the statistical analysis of the oral health epidemiological survey data is mainly based on the perspective of personal life history, as follows:

First, descriptive analysis

Children aged 3-5, middle school students aged 12-15, and adults aged 35-74 were randomly selected as survey sample data, and the oral health status of these three age groups was generally checked, and parents of children aged 3-5, 12-15 Middle school students aged 35-74 years old and adults aged 35-74 years old conduct questionnaire surveys on knowledge, attitudes and behaviors, through the display of basic statistical charts, basic statistical calculations, quantitative description and analysis of the overall situation of oral health epidemiology, and at the same time through basic statistical charts or basic statistical analysis Differences and prevalence characteristics of oral diseases in various age groups between urban and rural areas, age groups, gender, regions and other factors, through single factor and multi-factor analysis statistics of eating habits, living habits, past medical history, oral hygiene status of each age group , Oral knowledge and attitudes, social factors, education, income factors on the oral health of the respondents;

Second, in-depth statistical analysis

On the basis of the above-mentioned basic statistical analysis results, combined with the survey data of systemic chronic diseases, the correlation research between oral health and systemic diseases was carried out.

Furthermore, the study on the correlation between oral health and systemic diseases is based on in-depth data analysis from the perspective of life course to analyze the relationship between the physiological status of early growth and development of the human body and the occurrence and development of oral diseases. For example: the relationship between low birth weight and caries occurrence, the relationship between biological factors of early growth and development of individuals and tooth loss, tooth eruption mode, open bite, and tooth trauma.

Preferably, the calculation of the basic statistics includes mean value and ratio.

Further, when the inspection questionnaire is used at the oral health epidemiological investigation site, the personal information of the respondent, oral examination, oral health knowledge, attitude, and behavior survey data are entered. The personal information of the respondent includes ID No., name, gender, ethnicity, household registration type, occupation, years of education, date of birth, age; the financial management is used to fill in the application for medical equipment and consumables; the track management is used to upload photos and obtain current Location; the message management is used to check the messages sent by the oral health epidemiology project team; the log management is used for investigators to fill in their own work plan summary and log; the answered management is used for investigators to view The checklists and questionnaires that have not been uploaded are checked and checked; the offline management is used to manage the data entered by investigators in an offline environment, and select to upload to the storage transfer station in a WiFi environment.

Preferably, the storage transfer station is a central server, the data processing workstation is a background management computer terminal, and the data entry workstation includes a handheld tablet computer or a mobile phone, and the data entry workstation includes a handheld tablet computer or a mobile phone. The configuration of the tablet or mobile phone is Android 7.0 system, 32GB or above storage capacity, 3GB or above memory, and GPS positioning indicator. The invention installs the software in the hand-held tablet computer or the mobile phone, and uses the hand-held tablet computer as the data entry workstation.

The software of the present invention is reasonable and practical, and has a novel concept. When conducting a large-scale oral health epidemiological investigation, it can not only effectively obtain the investigation data, but also realize the precise data management of "and inspection, and entry, and proofreading, and uploading". It can save the secondary manual entry, eliminate the secondary entry error, and greatly save manpower, material resources, and financial resources; powerful background data, questionnaires, checklist data, financial, track, log, personal and other data are all available, no need to edit again , and can perform personalized searches, export relevant data, and conduct analysis; respondents can scan the QR code, "one person, one code", as the name implies, is a unique identification code owned by a surveyed respondent, which is also the " One person, one code" QR code. It is a diversified and integrated integrated security solution, which combines holographic anti-counterfeiting technology and two-dimensional code traceability system of "one person one code", which can effectively solve the problems of easy copying and imitation of ordinary two-dimensional codes. Then through the combination of the two levels of "two-dimensional code system platform + one person one code", the two-dimensional code can realize a high-level anti-counterfeiting function. Respondents only need to scan the QR code on the official account with their mobile phones, enter their name, gender, and region, and then they can query their own reports. They can query their own oral information and learn about their oral health status more quickly. , to quickly access the personal oral health feedback report and download it yourself.

The present invention fills up the gap in this research field, not only can effectively manage and store on-site survey data, facilitate the promotion and application of medical institutions in the whole province and even the whole country, provide a unified and standardized data platform for future data mining, but also improve project management and provide Investigate the performance management and financial management of doctors and other workers to provide evidence support.

Description of drawings

Fig. 1 is a structural representation of the present invention;

Figure 2 is the construction of the life course model of oral health knowledge, attitude and behavior;

In the figure 1. Data processing workstation, 2. Storage transfer station, 3. Data entry workstation, 4. Inspection questionnaire, 5. Financial management, 6. Leave management, 7. Track management, 8. Message management, 9. Log management, 10. Answered management, 11. Offline management.

Detailed ways

As shown in Figure 1, an oral health data collection analysis and follow-up system includes a data processing workstation 1 for oral health epidemiological investigation data storage and statistical analysis, a storage transfer station 2, and a data processing station for oral health epidemiological investigation. The data entry workstation 3 for collecting survey data, the data entry workstation 3 is connected with the data processing workstation 1 through a control signal through the storage transfer station 2, and the data entry workstation 3 includes inspection questionnaire 4, financial management 5, leave management 6, Trajectory management 7, message management 8, log management 9, answered management 10, offline management 11.

Further, the data analysis of the oral health epidemiological survey mainly consists of two modules: basic statistical analysis and in-depth statistical analysis, as follows:

First, basic statistical analysis

Randomly select children aged 3-5, middle school students aged 12-15, and adults over 15 years old as survey sample data, conduct a general examination of the oral health status of these three age groups, and conduct questionnaire surveys on their knowledge, attitudes, and behaviors. Calculation of basic statistics, display of basic statistical charts, quantitative description and analysis of the overall situation of oral epidemiology, and at the same time, through basic statistics or basic statistical charts, statistical analysis of the relationship between oral diseases in each age group, urban and rural areas, age groups, gender, and regions The differences between the population and the prevalence characteristics, through single factor and multi-factor analysis statistics of the dietary habits, living habits, past medical history, oral hygiene conditions, oral knowledge and attitudes, social factors, education, income factors in the oral cavity of the respondents. effects of health conditions;

Second, in-depth statistical analysis

On the basis of the above-mentioned basic statistical analysis results, combined with the survey data of systemic chronic diseases, the correlation research between oral health and systemic diseases was carried out.

Furthermore, the study on the correlation between oral health and systemic diseases is based on in-depth data analysis from the perspective of life course to analyze the relationship between the physiological status of early growth and development of the human body and the occurrence and development of oral diseases. For example: the relationship between low birth weight and caries occurrence, the relationship between biological factors of early growth and development of individuals and tooth loss, tooth eruption mode, open bite, and tooth trauma.

The steps of in-depth data analysis based on the perspective of life course are as follows:

1. By constructing a life course model framework, analyze the influence of the knowledge, attitude and behavior of the oral health of the respondents:

Life course theory is a theoretical approach that helps clarify how humans and societies develop through a combination of social age classes, short-term transitions, and historical epochs. The research paradigm of life course can be roughly divided into four aspects: "life in a specific time and space", "life of connection or coexistence", "timeliness of life" and subjective initiative in individual life choices. Life events are the main research object of life course theory, which refers to major events accompanied by relatively rapid changes and will bring serious and lasting effects.

1. Life events—the knowledge, attitude, and behavior of oral health affect the oral health knowledge, attitude, and behavior are closely related to oral health. For the respondents, the knowledge, attitude, and behavior of oral health are the An important "life event" on the trajectory becomes the "existing life experience and experience" of the respondent, and acts on "the individual's cognition of the social system and even the social environment" in the form of social memory, together with social situational factors. and actions", which ultimately affect the knowledge, attitudes and behaviors of the oral health of the respondents.

Hypothesis 1: The caries rate of respondents who answered "correctly" to the oral health knowledge of "bacteria can cause dental caries" is statistically different from that of respondents who answered "wrong".

2. Birth group effect—the influence of individual age In a society undergoing great changes, the social landscape presented to people born in different ages is different. Therefore, the social opportunities and Individuals are subject to social constraints are not the same. The principle of "life in a certain time and space" shows that the year in which an individual was born and which peer group he belongs to basically connects him with a certain historical force, which is an important part of the life course paradigm analysis. Because middle-aged people are more capable of accepting new things and learning than old people, their life and thinking styles are different, and their oral knowledge, attitudes, and behaviors are obviously stronger than those of old people.” There are obvious intergenerational differences in behavioral cognition; the level of education also has a significant impact on the oral knowledge, attitude, and behavioral cognition of the respondents.

Hypothesis 2: Compared with the elderly, middle-aged people pay more attention to protecting their own teeth and maintaining oral health.

3. Individual agency—the impact of personal choices and actions Life course theory opposes the social route as the sole determinant of an individual's life course, and believes that individual choices and actions are also very important in explaining this social expectation. Generally speaking, the time and sequence of certain events experienced by people of the same age also show great differences, which shows the important role of personal attributes in the study of life course. Generally speaking, socioeconomic factors have a significant positive impact on the respondents' oral knowledge, attitudes, and behavioral cognition.

Hypothesis 3: Respondents with stronger personal initiative have obvious advantages in their oral health status.

After the survey is completed, the questionnaire is decomposed question by question, and the important, prominent and recurring phenomena in the research survey are extracted through codes, and the meanings of these phenomena are explained. The coding process consists of three levels of coding, namely open coding, spindle coding and selective coding. Open coding refers to the generation of initial concepts and the formation of conceptual categories from the on-site questionnaire by coding and reorganizing the on-site questionnaire item by item. In this process, the initial concept was obtained in the construction of the influencing factor model of the life course of the respondent. Specific categories were obtained through conceptual attribution, meaning deduplication, data aggregation, data screening, and concept categorization of the on-site questionnaire. If among study variables and descriptive statistics:

(1) The explained variable is the respondent’s oral knowledge, attitude, and behavior cognition. The question in the questionnaire is “is it normal to have bleeding gums when brushing your teeth?” and the options are “①Correct; ②Incorrect; ③ do not know". Merge the three options into two categories. The first category is to combine ① "correct" and assign a value of 1; the second category is to combine ② and ③ into "no" and assign a value of 0. If 68% of the respondents choose "correct", 32% of the respondents choose "wrong".

(2) The independent variable is life course. According to the analysis framework, the life course variables are specifically operationalized into the following categories. ①Life events: the awareness rate of oral health knowledge in the questionnaire survey of the respondents, which is a binary dummy variable (≥85%=1, <85%=0), with 0 as the reference; 60% of those surveyed, and 40% of those surveyed were aware of oral health knowledge. ②Individual initiative: education level: transform the education level into a dichotomous dummy variable (junior high school and above = 1, and below junior high school = 0), with 0 as the reference; those who are junior high school and below are 27%, and those who are above junior high school are 73%.

(3) Control variables. If you choose to participate in the control variables of the model from the two levels of basic personal characteristics and family characteristics, including health status, marital status, whether you work in agriculture, and the number of children. For example, 45% are farmers and 55% are non-farmers; 40% are spouses and 60% are unmarried; 40% are adults with 0-2 children, and 45% are adults with 3-4 children , 15% of the elderly have more than 4 children.

Based on the above data for model construction (see Figure 2), if the explanatory variables are divided into three categories, namely, rural areas, urban suburbs and cities, a multivariate logistic model is used to compare urban and rural areas, urban and rural areas, and urban and rural areas. Differences in oral health knowledge, attitudes, and behaviors between urban and suburban areas, Model I and Model II were established with rural areas as the reference category, and Model III was established with urban and suburban areas as the reference category (see Table 1). After the survey is completed, the questionnaire is decomposed question by question, and the important, prominent and recurring phenomena in the research survey are extracted through codes, and the meanings of these phenomena are explained. The coding process consists of three levels of coding, namely open coding, spindle coding and selective coding. Open coding refers to the generation of initial concepts and the formation of conceptual categories from the on-site questionnaire by coding and reorganizing the on-site questionnaire item by item. In this process, the initial concept was obtained in the construction of the influencing factor model of the life course of the respondent. Specific categories were obtained through conceptual attribution, meaning deduplication, data aggregation, data screening, and concept categorization of the on-site questionnaire. Construct a baseline model in which the influencing variables only include control variables, and then add the core influencing variables of research focus—life course factors on the basis of the baseline to establish a complete model. If the model fitting results are as follows, see Table 1.

Table 1 Influencing factors of oral health knowledge, attitude and behavior (OR)

Notes: 1. *, ** and ** indicate that the corresponding variables are significant at the 1%, 5% and 10% levels respectively; the fitting results that do not show a significant effect at the 0.1 significance level are not shown in the table; " ..." indicates variables that did not show a significant effect in any of the six models.

From the benchmark model, it can be seen that under the premise of only considering the control variables, health status and education level have a significant impact on the oral health knowledge, attitude and behavior of the respondents. Based on the above research, we have learned that it is imperative to strengthen education for all and increase oral health education and publicity.

2. Through the "cumulative advantage and disadvantage effect" of the life course, the cumulative risk model of the life course approach

Currently, research on social inequalities in oral health has introduced a life-course perspective. Under this theoretical framework, the past experiences of the respondents are analyzed in the context of the historical time and space in which they live, in order to find a way to connect the individual meaning of life with the social meaning. The theoretical perspective of the life course is clearly highlighted, and personal characteristics and transformations are accompanied by a lifetime. Linked to social stratification, at the individual level, the degree of inequality experienced by individuals increases or decreases with age; at the group level, differences between individuals also change over time.

As far as the research on the gradient of oral health in this paper is concerned, the socioeconomic factors of individuals will not be stable throughout their lives, while oral health generally shows a downward trend. The analysis of oral health should focus on the entire life span, placing socioeconomic factors, diet structure factors, medical resources and natural environment, and combined with individual age, birth generation and historical period to analyze. Life-course research focuses on disease mechanisms underlying the inequalities between oral and systemic diseases. "Cumulative advantages and disadvantages effect" is a hypothesis that has been developed systematically and has been widely verified. This concept was first proposed by Merton in his classic paper "The Matthew Effect in Science". Cumulative advantage means that the advantageous resources of a certain group will accumulate over time, which means that the unequal distribution gap of the advantageous resources will increase over time. This so-called "advantage" refers to some key resources and rewards, such as academic reputation, wealth or health. These social inequalities are generated by social institutions and occur throughout the life course in the process of social stratification. In the process of stratification, "disadvantages (of resource possession) will increase the risks faced by individuals, and advantages will increase the opportunities faced by individuals". The concept of cumulative advantages and disadvantages reflects and transcends the traditional adage that the poor get poorer and the rich get richer. In the process of accumulating advantages and disadvantages, the initial subtle differences magnify over time, which makes it difficult for individuals or groups who are at a disadvantage in education and health in the early stage to catch up. Therefore, the early inequality of social resources such as education will dynamically affect the subsequent occupation, income and wealth accumulation, thus putting a disadvantaged group in a long-term or even more disadvantaged situation, so that oral diseases and systemic diseases of the disadvantaged groups The proportion of patients with diseases has increased.

Which of the two competing theories in the life course—the "cumulative advantage/disadvantage theory" and the "age-neutralizing effect theory"—was more effective in detecting the interaction between education level stratification and per capita GDP stratification? Explaining oral health inequalities experienced in GDP per capita. Through the development and management of electronic data follow-up system, tracking data and growth curve model, it can be found that socioeconomic status stratification has a unique impact on oral health inequality and development caused by gender stratification.

Firstly, the dependent variable is set. If the self-assessed oral health is the dependent variable, even if the 1-4 coding is used, it is mostly treated as a continuous variable. The treatment of oral health continues this tradition to facilitate model simplification and interpretation. However, in order to test the robustness of the growth curve model, we also regard health as an ordinal variable for analysis, using a multi-layer cumulative Logistic regression model, and the results obtained are not much different from treating health as a continuous variable.

Self-assessment of oral health status is the health index of this study. Self-assessment of oral health is a subjective health assessment, which is considered to be an effective predictor of oral disease status and a relatively comprehensive and effective measure of oral health. In the questionnaire survey in the electronic data follow-up system, the respondents answered the question "How do you evaluate your oral health status", and the options are "poor", "general", "good" and "very good". In the analysis we treated oral health as a continuous variable, coded from 1 to 4 in order from "poor" to "very good".

The independent variable, education level, is a continuous variable, that is, "how many years of reading", and the education level of adults does not change over time. We use the education level reported by the respondents when they entered the survey last time. Income is the per capita household income of respondents in the previous year. Taking into account the inflation factor, the annual household income in each observation year was converted to the income level in 2010, so as to facilitate the longitudinal comparison between the years. Annual household income is a covariate of time. In order to avoid the influence of extreme values, we take the natural logarithm of household income in the analysis. To control for age, generation, and other variables, we take a sample of adults over the age of 35 at the time of the survey. During this period, most people have completed college education, which can effectively avoid the impact of healthy choices on education. In the subsequent analysis of the growth curve model, the age is centralized, that is, concentrated to the sample mean, so as to facilitate the interpretation of the intercept parameter in the result. Age was squared to estimate the quadratic effect of age on oral health. However, the square term of age has no significant effect on oral health changes, so it is not included in the subsequent analysis model. Birth generation is an important concept in the analysis of life course. Marital status is thought to have a greater impact on health. We coded "married" as 1 and "other conditions (single, divorced, widowed)" as 0. Urban-rural duality is also considered to be an important factor affecting health. We use household registration as a control variable, with “urban household registration” coded as 1 and “rural household registration” coded as 0.

We use a growth curve model to examine systematic differences in individual health trends due to socioeconomic factors. The statistical software used is the gender difference of social stratification and health inequality. The growth curve model, also known as the hierarchical linear model, is a multi-layer analysis model, which is used to deal with the change of personal data in longitudinal data over time. Individual data are repeatedly observed and recorded in the survey, so this data has a hierarchical structure, that is, data from different years are nested within individuals. Thus, the growth curve model allows us to explore both intra-individual and inter-individual changes in oral health. Another great advantage of this model is to deal with "unbalanced data", that is, each individual can have different number of observations, so the use of growth curve model can maximize the use of longitudinal data information. The growth curve model is composed of a pair of sub-models: the first layer model shows the change of personal data over time, and the second layer model reflects the difference in the change trend of personal data among different individuals. The model assumes that there are rules to follow in the changing patterns of personal data. Models of personal growth have different starting points (different intercepts) and different rates of change in personal growth (different slopes). That is, the intercept and slope vary randomly between individuals. For this study, some people have good oral health status and some people have poor oral health status in the initial year (intercepts are high and low), and as the age increases, some people's oral health status changes quickly and some people change slowly. In addition to the dependent variable oral health, the independent variable family income also changes over time, which is called a covariate between individuals, and there are also intra-individual and inter-individual changes. Stratified differences in time covariates were also distinguished. To estimate the trajectory of individual oral health with age and the heterogeneity of oral health trajectory due to gender and socioeconomic status, the growth curve model formula used in this study is as follows. First layer model:

Among them, i represents the survey individual from 1 to N samples; Healthti represents individual i at time

t; Ageti is the age of individual i at time t, but after centering (subtracting the mean age of 48.1 years); Incometi is the logarithm of household income of individual i at time t. For a specific individual i, the coefficient π0i represents its oral health score at the average age, that is, the intercept of personal oral health; π1i is the slope of personal oral health with age; π2i is the corresponding increase in income (log value) The expected value of oral health change; π3i represents the expected value of the slope of oral health change caused by the interactive variables of income and age; eti is the residual error of a specific individual i at time t, which obeys the normal distribution with mean 0 and variance σ. Other time-varying control variables Xj are placed in the first-level model, including marital status for each measurement and death in the next follow-up survey. The first-level model mainly measures the trajectory of the individual's own health with age. To measure the heterogeneity of individual health trajectory changes and to detect the impact of gender and individual-level socioeconomic characteristics on individual health change trajectories, the influence of individual characteristics on the individual intercept and slope parameters in the first-level model was measured. The second-level model contains the following series of formulas. The second layer model:

The second-level model contains four formulas, among which formula (2) measures the intercept parameter π0i in the first-level model, and formula (3), formula (4), and formula (5) measure the intercept parameter π0i in the first-level model respectively. The slope parameters of π1i, π2i, π3i. The parameter βpq is a fixed-effect model parameter, which represents the influence of individual characteristics such as gender and education on the intercept and slope parameters in the first-level model. β00-β03 are the parameters of the intercept model π0i, which measure the impact of gender, education, and the interactive variables of gender and education on the intercept. Other individual-level control variables Zj that do not change with age, such as generation, household registration, and region, are also included in the intercept parameter model. β10-β13 is the π1i parameter of the above-mentioned gender, education, and "gender × education" variables on the slope of healthy growth, and it is also the interaction effect of these variables and age. β20 and β21 measure the interaction parameters of gender and family income (time covariate π2i), while β30 and β31 measure the three-dimensional interaction parameters π3i of sex, income and age. γ10 and γ1i are random effects of intercept and primary slope, which also obey normal distribution with mean 0. γ10, γ1i and eti in formula (1) together constitute the variance of the random effect. As far as research hypotheses are concerned, β03 and β20 are parameters to verify hypothesis 1, β03 and π31 are parameters to verify hypothesis 2, and β12, β13, β30 and β31 are parameters to verify hypothesis 3.

Through the electronic data collection system, we use the tracking data across the years to track the impact of socioeconomic status and gender on the changes in individual oral health during the life course. To know whether socioeconomic factors have consistent oral health returns for men and women over the life course, and whether these effects increase with age.

Analyze the early socioeconomic, social psychological and behavioral factors of individuals to predict their later oral health status, such as caries, periodontal disease, tooth loss, tooth trauma, open occlusion, tooth eruption, and even It's a toothache.

Preferably, the calculation of the basic statistics includes mean value and ratio.

Further, when the inspection questionnaire 4 is used on the oral health epidemiological investigation site, the personal information of the respondent, oral examination, and oral health KAP survey are entered. The personal information of the respondent includes ID number, name, Gender, ethnicity, household registration type, occupation, years of education, date of birth, age; the financial management 5 is used to fill in the application for medical equipment and consumables; the track management 7 is used to upload photos and obtain the current location; The message management is used to check the messages sent by the oral health epidemiology project team; the log management 9 is used for investigators to fill in their own work plan summary and log; the answered management 10 is used for investigators to view Unuploaded checklists and questionnaires are checked and checked; the offline management 11 is used to manage the data entered by investigators in an offline environment, and select to upload to the storage transfer station 2 in a WiFi environment.

Preferably, the storage transfer station 2 is a central server, the data processing workstation 1 is a background management computer terminal, the data entry workstation 3 includes a handheld tablet computer device or a mobile phone, and the data entry workstation 3 includes a handheld tablet computer or a mobile phone , the configuration of the handheld tablet computer or mobile phone is an Android 7.0 system, a storage capacity of 32GB or above, a memory of 3GB or above, and a GPS positioning indicator. The present invention installs the software in the handheld tablet computer or mobile phone, and uses the handheld tablet computer as the data entry workstation 3 with the configuration terminal.

Claims (7)

1. The oral health data acquisition and analysis method is characterized by comprising a data processing workstation for oral epidemiological survey data storage and statistical analysis, a storage transfer station and a data entry workstation for acquiring oral epidemiological survey data, wherein the data entry workstation is connected with the data processing workstation through a control signal through the storage transfer station, and the data entry workstation comprises an examination questionnaire, financial management, leave-on management, trajectory management, message management, log management, answered management and offline management.

2. The method for collecting and analyzing oral health data according to claim 1, wherein the statistical analysis of oral epidemiological survey data mainly comprises two modules of basic statistical analysis and deep statistical analysis, specifically as follows:

first, basic statistical analysis

Randomly extracting children aged 3-5, middle school students aged 12-15 and adults aged more than 15 as survey sample data, checking oral health conditions of the three age groups, performing questionnaire survey on knowledge, attitude and behavior of the children, quantitatively describing and analyzing oral epidemiological overall conditions in a basic statistic calculation and basic statistic chart display mode, analyzing differences and prevalence characteristics of oral diseases of each age group in urban and rural areas, age groups, sex and regions in a statistical manner through the basic statistic or the basic statistic chart, and analyzing and counting the influences of dietary habits, living habits, past disease history, oral hygiene conditions, oral knowledge and attitude conditions, social factors, academic history and income factors of each age group on the oral health conditions of the testees through single factor and multi-factor;

second, statistical analysis of depth

On the basis of the basic statistical analysis results, the correlation study of oral health and systemic diseases is carried out by combining the survey data of systemic chronic diseases.

3. The electronic data collection, analysis and follow-up system for oral health as claimed in claim 2, wherein the research on the correlation between oral health and general diseases is to deeply analyze the relationship between the physiological status of human early growth and development and the occurrence and development of oral diseases by using a critical phase model of a life history method.

4. The cavity health electronic data collection, analysis, and follow-up system of claim 2, wherein the basic statistics calculations include mean and ratio.

5. The system for electronic data collection, analysis and follow-up of oral health as claimed in claim 1, wherein the examination questionnaire is used for entering personal information of the examinee, oral examination and oral health KAP survey, when the oral health epidemiological survey is on site, wherein the personal information of the examinee includes ID number, name, gender, ethnicity, family type, occupation, education age, birth date and age; the financial management is used for filling in and applying for medical equipment, consumables and other contents; the track management is used for uploading the photos and acquiring the current position; the message management is used for checking and receiving messages sent by the oral health epidemiological project group; the log management is used for filling the work plan summary and the log of the investigator; the answered management is used for checking the inspection list and the questionnaire which are not uploaded by the investigator, and performing inspection and checking; and the offline management is used for managing the data input by the investigator in the offline environment and selectively uploading the data to the storage transfer station in the WiFi environment.

6. The cavity health electronic data acquisition, analysis and follow-up visit system as claimed in claim 1, wherein the storage transfer station is a central server, the data processing workstation is a background management computer terminal, and the data entry workstation comprises a handheld tablet computer or a mobile phone.

7. The cavity health electronic data acquisition, analysis and follow-up visit system according to claim 6, wherein the configuration of the handheld tablet computer or mobile phone is system android 7.0, 32GB and above storage capacity, 3GB and above memory, GPS positioning indicator.

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