CN110634541A

CN110634541A - Oral health data acquisition and analysis method

Info

Publication number: CN110634541A
Application number: CN201910557755.4A
Authority: CN
Inventors: 王翔宇; 赵彬; 侯如霞; 任文娟; 刘君瑜; 李然; 刘佳佳; 杨婷婷; 史晓彤; 杜嘉琪; 李玉芳; 茹媛梦; 张宁; 柳海凤
Original assignee: Shanxi Medical University
Current assignee: Shanxi Medical University
Priority date: 2019-06-26
Filing date: 2019-06-26
Publication date: 2019-12-31

Abstract

The invention discloses an oral health data acquisition and analysis method, which comprises a data processing workstation for storing, counting and analyzing oral health epidemiological survey data, a storage transfer station and a data entry workstation for acquiring the oral health 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, track management, message management, log management, answered management and off-line management. The invention fills the blank of the research field, can effectively manage and store the on-site survey data, is convenient for popularization and application in medical institutions of the whole province and even the whole country, provides a unified and standard data platform for future data mining, improves project management, and provides evidence support for performance management and financial management of workers such as survey doctors and the like.

Description

Oral health data acquisition and analysis method

Technical Field

The invention belongs to the field of oral cavity data acquisition and analysis, and particularly relates to an oral cavity health data acquisition and analysis method.

Background

In order to master the oral health condition of residents in China, four national oral health epidemiological surveys were developed in 1983, 1995, 2005 and 2015 in China respectively, and scientific basis is provided for formulating oral hygiene policies. However, in the four large surveys, paper questionnaires and check lists are adopted, secondary manual entry is needed after the surveys are finished, systematic software management is lacked in financial management, track management and the like, and certain work difficulty is increased for workers such as survey doctors in field survey work and later period management.

Disclosure of Invention

In order to overcome the defects that the conventional paper questionnaire survey and check sheet needs secondary manual input, is lack of uniformity in management and is inconvenient to look up in the later period, the invention provides the oral health data acquisition and analysis method which can effectively acquire survey data, realize accurate data analysis and management, save secondary manual input, eliminate secondary input errors and greatly save manpower, material resources and financial resources.

The invention adopts the following steps in order to realize the purpose:

an oral health data acquisition and analysis method comprises a data processing workstation for oral health epidemiological survey data storage and statistical analysis, a storage transfer station and a data entry workstation for acquiring oral health 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-asking management, trajectory management, message management, log management, answered management and offline management.

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

first, descriptive analysis

Randomly extracting children of 3-5 years old, middle school students of 12-15 years old and adults of 35-74 years old as survey sample data, carrying out general examination on oral health conditions of the three age groups, carrying out questionnaire survey on knowledge, attitude and behavior of children of 3-5 years old, middle school students of 12-15 years old and adults of 35-74 years old, quantitatively describing and analyzing oral health epidemiological overall conditions by a basic statistical chart display mode and basic statistical quantity calculation, simultaneously analyzing differences and epidemic characteristics of oral diseases of various age groups among different factors such as urban and rural areas, age groups, sex and regions by a basic statistical chart or a basic statistical quantity, and carrying out statistics on dietary habits, living habits, past disease history, oral health conditions, oral knowledge and attitude conditions, social factors, oral health conditions and attitude conditions of various age groups by single factor and multi-factor analysis, Influence of the study history and income factors on the oral health condition of the respondents;

second, depth statistical analysis

Based on the basic statistical analysis results, the correlation between oral health and systemic diseases is studied by combining survey data such as systemic chronic diseases.

Furthermore, the research on the correlation between the oral health and the whole body disease analyzes the relationship between the physiological condition of the human body in early growth and development and the occurrence and development of the oral disease based on the depth data under the view point of life history. For example: relationship between low birth body mass and caries occurrence, relationship between biological factors of early growth and development of individuals and tooth loss, tooth eruption mode, open and close of teeth and tooth trauma.

Preferably, the basic statistic calculation includes a mean and a ratio.

Further, the examination questionnaire is used for inputting personal information, oral examination, oral health knowledge, attitude and behavior survey data of the examinee in an oral health epidemiological survey field, wherein the personal information of the examinee comprises an ID number, a name, a gender, a ethnicity, a family type, an occupation, an educational age, a birth date and an 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.

Preferably, the storage transfer station is a central server, the data processing workstation is a background management computer end, the data entry workstation comprises a handheld tablet computer device end or a mobile phone, the data entry workstation comprises a handheld tablet computer or a mobile phone, and the configuration of the handheld tablet computer or the mobile phone is that the system is an android 7.0 system, a storage capacity of 32GB or more, a memory of 3GB or more, and a GPS positioning indicator. The invention installs the software into the handheld tablet personal computer or the mobile phone, and takes the configuration end of the handheld tablet personal computer as a data entry workstation.

The software of the invention is reasonable and practical, the conception is novel, when the large-scale oral health epidemiology investigation is carried out, the investigation data can be effectively obtained, the accurate data management of 'and inspection, and input, and proofreading and uploading' is realized, the secondary manual input can be saved, the secondary input error is eliminated, and the manpower, material resources and financial resources are greatly saved; powerful background data, questionnaire, check list data, finance, track, log, personal data and the like are all consistent, do not need to be edited again, and can be searched in a personalized manner, relevant data are exported and analyzed; the examinee can scan the two-dimensional code, and the 'one-person-one-code' is an identification code which is unique for the examinee who adjusts the oral cavity flow, and is also a 'one-person-one-code' two-dimensional code. The method is an integrated safety solution of diversified integration, combines a holographic anti-counterfeiting technology and a one-person-one code of a two-dimension code traceability system, and can effectively solve the problems that a common two-dimension code is easy to copy and the like. And then two layers of a two-dimension code system platform and a one-person one-code are combined, so that the two-dimension code realizes a high-level anti-counterfeiting function. The examinee can inquire the report of the examinee only by scanning the two-dimensional code on the public number through the mobile phone and inputting the name, the gender and the region, and can quickly know the oral health condition of the examinee by inquiring the oral information of the examinee. And rapidly accessing and automatically downloading the oral health condition feedback report of the individual.

The invention fills the blank of the research field, can effectively manage and store the on-site survey data, is convenient for popularization and application in medical institutions of the whole province and even the whole country, provides a unified and standard data platform for future data mining, improves project management, and provides evidence support for performance management and financial management of workers such as survey doctors and the like.

Drawings

FIG. 1 is a schematic structural view of the present invention;

FIG. 2 is a life history model construction of oral health knowledge, attitude and behavior;

in the figure, 1, a data processing workstation, 2, a storage transfer station, 3, a data entry workstation, 4, an examination questionnaire, 5, financial management, 6, leave management, 7, track management, 8, message management, 9, log management, 10, answered management and 11, offline management are shown.

Detailed Description

As shown in fig. 1, an oral health data acquisition, analysis and follow-up system comprises a data processing workstation 1 for oral health epidemiological survey data storage and statistical analysis, a storage transfer station 2, and a data entry workstation 3 for acquiring oral health epidemiological survey data, wherein the data entry workstation 3 is connected with the data processing workstation 1 through the storage transfer station 2 by a control signal, and the data entry workstation 3 comprises an examination questionnaire 4, a financial management 5, a leave-asking management 6, a trajectory management 7, a message management 8, a log management 9, an answered management 10, and an offline management 11.

Further, the analysis of the oral health epidemiological survey data mainly comprises two modules, namely a basic statistical analysis module and a deep statistical analysis module, and specifically comprises the following steps:

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, carrying out general examination on the oral health conditions of the three age groups, carrying out questionnaire survey on the knowledge, attitude and behavior of the children, quantitatively describing and analyzing the oral epidemiological overall situation through basic statistic calculation and a basic statistical chart display mode, simultaneously carrying out statistical analysis on the difference and the epidemic characteristics of oral diseases of each age group among different factors such as urban and rural areas, age groups, sex and regions through the basic statistic or the basic statistical chart, and carrying out statistical analysis on the influence of dietary habits, living habits, past medical history, oral health conditions, oral knowledge and attitude conditions, social factors, academic history and income factors of each age group on the oral health conditions of the respondents through single factor and multi-factor;

second, depth statistical analysis

The steps of the depth data analysis based on the life history view angle are as follows:

firstly, analyzing the influence of knowledge, attitude and behavior of the oral health of a person to be investigated by constructing a life history model framework:

the life history theory is a theoretical orientation that helps to clarify how humans and society develop under the combined actions of age classes, short-term transitions and historical times. The study paradigm of life history can be broadly divided into four areas: "life in the air at a specific time", "connected or coexisting life", "timeliness of life" and subjective initiative in personal life selection. A life event is a major research subject of life history theory, and refers to a major event accompanied by relatively sharp changes and having a serious and persistent effect.

1. The oral health knowledge, attitude and behavior are closely related to the oral health, and for the examinee, the oral health knowledge, attitude and behavior are an important 'life event' on the life track, become the examinee 'existing life experience and experience', and act on 'the cognition and action of the individual on the social system and even the social environment' together with the social situation factors in a social memory manner, so that the oral health knowledge, attitude and behavior of the examinee are finally influenced.

Suppose that the respondents who answer "correct" to the knowledge of oral health that "bacteria can cause dental caries" have a statistical difference in caries rate from the respondents who answer "wrong".

2. Birth group effect-the effect of age of an individual in society undergoing tremendous transition, the social landscape presented to them for people born in different ages is not the same, and thus, the social opportunities that the individual has and the social limitations that the individual is subjected to are also different. The principle of "timed air life" indicates that the year in which an individual is born and belongs to which age group essentially relates it to some historical strength, which is an important component of life history paradigm analysis. The ability of middle-aged people to accept new things and study is stronger than that of old people, the life and thinking ways of the middle-aged people are different, and the oral knowledge, attitude and behavior of the middle-aged people are obviously stronger than that of the old people, so that the age enables respondents to have obvious interpersonal difference in oral knowledge, attitude and behavior cognition, and the education degree also has obvious influence on the respondents in oral knowledge, attitude and behavior cognition.

And the second assumption is that the middle-aged people pay more attention to protecting their teeth and maintaining oral health compared with the elderly.

3. Personal motility-the life history theory of individual choice and action object to treating social routes as the only determinants of an individual's life history, it is believed that individual choice and action are also important in the interpretation of this social expectation. Generally, people of the same age experience greater differences in the timing and sequence of certain events, which indicates the important role of personal attributes in life history research. Generally speaking, socio-economic factors have significant positive correlation effects on oral knowledge, attitude and behavioral cognition of respondents.

Suppose that the more powerful the individual is, the oral health of the respondent has significant advantages.

After the investigation is finished, the questionnaire is decomposed subject to subject, important, prominent and repeated phenomena in the research investigation are extracted through coding, and the significance of the phenomena is explained. The encoding process consists of 3 levels of encoding, namely open-ended encoding, spindle encoding and selective encoding. The open code is to encode and recombine a field questionnaire on a topic-by-topic basis to generate an initial concept from the field questionnaire and form a concept category. In the process, an initial concept is obtained in the construction of the influence factor model of the life history of the respondent. The specific category is obtained through concept attribution, meaning de-duplication, data summarization, data screening and concept domain of the on-site questionnaire. If in the study variables and descriptive statistics:

(1) the explained variables are knowledge, attitude and behavior cognition of the examinee in the oral cavity, and the question in the questionnaire is 'whether gum bleeding is normal during tooth brushing', the options are 'correct', 'incorrect' and 'unknown'. The three options are merged into two classes, the first class is that the sum of the first two is 'correct' and is assigned as 1, and the second class is that the sum of the second two and the third two is merged into 'not' and is assigned as 0. If 68% of the respondents are selected as "correct", 32% of the respondents are selected as "incorrect".

(2) The independent variable is the life history. According to the analysis framework, the life history variables are embodied into the following classes. The oral health knowledge rate in the questionnaire survey of the respondents is a binary virtual variable (more than or equal to 85% =1 and less than 85% =0) and 0 is used as a reference, and if the respondents of the oral health knowledge rate are 60% and the respondents of the oral health knowledge rate are 40%. Second, individual motility, education degree, which is the degree of education converted into two-class virtual variables (junior and above =1, junior and below =0) with reference to 0, 27 for junior and below, and 73% for junior and above.

(3) And controlling the variable. If control variables participating in the model from two levels of personal basic characteristics and family characteristics are selected, the control variables comprise health conditions, marital conditions, whether to take care of farmers and the number of children. Suppose that 45% of presidents, 55% of non-presidents, 40% of non-presidents, 60% of non-presidents, 40% of adults with 0-2 children, 45% of 3-4 children, and more than 4 children in 15% of the elderly.

Model construction is carried out based on the data (see figure 2), if the explained variables are divided into three types, namely rural areas, suburban areas and cities, so that the multivariate logistic model is adopted to compare the differences of oral health knowledge, attitudes and behaviors between the cities and the rural areas, between the suburban areas and the rural areas and between the cities and the suburban areas, the rural areas are used as a reference type to establish a model I and a model II, and the suburban areas are used as a reference type to establish a model III (see table 1). After the investigation is finished, the questionnaire is decomposed subject to subject, important, prominent and repeated phenomena in the research investigation are extracted through coding, and the significance of the phenomena is explained. The encoding process consists of 3 levels of encoding, namely open-ended encoding, spindle encoding and selective encoding. The open code is to encode and recombine a field questionnaire on a topic-by-topic basis to generate an initial concept from the field questionnaire and form a concept category. In the process, an initial concept is obtained in the construction of the influence factor model of the life history of the respondent. The specific category is obtained through concept attribution, meaning de-duplication, data summarization, data screening and concept domain of the on-site questionnaire. And constructing a reference model with influence variables only including control variables, then adding a core influence variable-life history factor of research interest on the basis of the reference, and establishing a complete model, if the fitting result of the model is as follows, see table 1.

TABLE 1 influence factors (OR) of oral health knowledge, attitude, behavior

Note that 1, T, and T represent the respective variables significant at the 1%, 5%, and 10% levels, respectively, the fitting results that did not exhibit significant effect at the 0.1 significance level are not shown in the table, and "… …" represents the variable that did not exhibit significant effect in any of the six models.

From the reference model, it can be seen that the health condition and education degree have significant influence on the oral health knowledge, attitude and behavior of the examinee on the premise of only considering the control variables. The inspiration given to us based on the above-mentioned research is that the national education is enhanced and the oral health education propaganda education is imperative.

Secondly, through 'accumulated advantage and disadvantage effects' of life history, danger accumulation model of life history method

At present, social inequality studies of oral health have introduced perspectives of life histories. Under the theoretical framework, the past experience of the respondents is analyzed in the historical time and space background of the lives of the respondents, so as to find a way to connect the individual significance of the lives with the social significance. The theoretical perspective of life history clearly highlights that individual features and transitions are life-long. In connection with social stratification, at the individual level, the inequality experienced by an individual increases or decreases with age; at the population level, the differences between individuals also vary over time.

For the purposes of the oral health gradient study, the socio-economic factors of an individual will not be stable during a lifetime, oral health generally tends to decline, and the analysis of oral health should be focused on the entire life span, placed in multiple levels of socio-economic factors, dietary structural factors, medical resources and natural environment, and analyzed in combination with the age, birth generation and historical period of the individual. Studies of life history concern disease mechanisms that are not equal between oral diseases and systemic diseases throughout the body. The "cumulative superiority and inferiority effect" is a hypothesis developed by the system and is verified more. This concept was originally proposed by morton in his classical thesis "horse-solar effect in science". Cumulative dominance means that a group has dominant resources that accumulate over time, meaning that the unequal allocation gap of the dominant resources increases over time. This so-called "superiority" refers to some key resource and reward, such as academic reputation, wealth or health. These social unequalities are generated by social institutions and throughout the life history during social stratification. In the course of the layering process, "(resource-owned) disadvantages increase the risk faced by the individual, while advantages increase the chance faced by the individual". The concept of cumulative advantages and disadvantages reflects and surpasses the traditional 'poor, more poor, and richer' allegiance. In the process of accumulation of superiority and inferiority, the initial subtle differences amplify over time, which makes it difficult for individuals or groups at an early stage that are at a disadvantage in education, health, etc. to catch up. Therefore, the inequality of the early social resources such as education will dynamically affect the subsequent occupations, income and wealth accumulation, thereby placing a vulnerable group in a long-term or even more vulnerable situation, and thus increasing the proportion of patients suffering from oral diseases and systemic diseases in the vulnerable group.

If the interaction between the education level hierarchy and the human-average GDP hierarchy is detected, two competitive theories in the life process, namely an accumulative superiority/inferiority theory and an age neutralization effect theory, can explain the inequality of oral health experienced by the human-average GDP. Through the development and management of an electronic data follow-up system, a data tracking and growth curve model can be found, and the social and economic status layering has unique influence on the unequal oral health and the development thereof caused by gender layering.

First, if the self-assessed oral health is a dependent variable, it is often treated as a continuous variable even if 1-4 codes are used. The treatment of oral health continues this tradition to facilitate model simplification and interpretation. However, for the robustness test of the growth curve model, we also analyzed health as a sequencing variable, using a multi-layer cumulative Logistic regression model, with results that are not very different from those obtained by treating health as a continuous variable.

Self-assessed oral health status is a health indicator for this study. The self-evaluation of the oral health is a subjective health evaluation, is considered as an effective prediction index of the oral disease condition, and is a relatively comprehensive and effective oral health measurement index. In questionnaires in an electronic data follow-up system, the respondents answer the question "how you evaluate their oral health condition", and the alternatives are "poor", "normal", "good", and "very good". In the analysis we treated oral health as a continuous variable, coding from 1 to 4 in order from "poor" to "very good".

The independent variable education level is a continuous variable, i.e. "how many years of books are read", the adult education level does not change with time, and we use the education level reported by the last time the respondent entered the survey. The income is the per-visitor family income in the previous year. The annual income of the family for each observation year is converted to an income level of 2010, taking into account the inflation factor of the currency, so as to facilitate longitudinal comparison between the years. Family annual income is a covariate of time. To avoid extreme effects, we take natural logarithms of household revenue in the analysis. Age, generation, and other control variables, we take samples of adults over 35 years of age at the time of investigation. In this period, people mostly finish university education, and the influence of health selection on education can be effectively avoided. In the later analysis of the growth curve model, the age is centered, i.e. concentrated to the sample mean, so as to facilitate the interpretation of the intercept parameters in the results. Age was squared to estimate the quadratic effect of age on oral health. The age squared term, however, does not significantly affect oral health changes and is not included in the analytical models that follow. In the analysis of life history, the generation of birth is a very important concept. Marital conditions are considered to have a greater impact on health. We encode "married" as 1 and "other cases (not married, dissimilarity, funeral couple)" as 0. Urban and rural dualization is also considered as an important factor influencing health, and the household registration is used as a control variable, the urban household opening is coded as 1, and the rural household opening is coded as 0.

We used a growth curve model to examine the systematic differences in the trend of changes in individual health due to socioeconomic factors. The used statistical software is gender difference of unequal social stratification and health, and the growth curve model is also called a stratified linear model and is a multilayer analysis model used for processing the condition that personal data in longitudinal data change along with time. The data of an individual is repeatedly observed and recorded in the survey, so the data has a layered structure, namely, the data of different years are nested in the individual. Thus, the growth curve model allows us to simultaneously explore oral health changes within and between individuals. Another great advantage of this model is that it deals with "unbalanced data", that is, each individual can have a different number of observations, and therefore the use of a growing curve model can make maximum use of the information of the longitudinal data. The growth curve model consists of a pair of sub-models: the first layer model shows that the personal data change along with time, and the second layer model reflects the difference of the change trend of the personal data among different individuals. The model assumes that the changing pattern of personal data is nutatively traceable. The model of the individual growth has different starting points (different intercepts) and the rate of the individual growth change is also different (different slopes). That is, the intercept and slope vary randomly between individuals. For the study, oral health was good in the initial years, poor in the initial years (high or low intercept), and changed rapidly and slowly with age. In addition to the dependent variable oral health, independent variable household revenue also varies with time, which is called covariate, as well as within an individual and between individuals. Hierarchical differences in time covariates are also distinguished. In order to estimate the variation track of the oral health of an individual with age and the heterogeneity of the oral health track caused by gender and socioeconomic status, the development curve model formula adopted by the research is as follows. A first layer model:

wherein i represents the investigational individual from 1 to N samples; healthti represents individual i over time

Health measurement of t; ageti is the age of individual i at time t, but is centralized (minus the mean age 48.1 years); incometti is the household revenue log value for individual i at time t. For a particular individual i, the coefficient pi 0i represents its oral health score at the average age, i.e., the intercept of the individual's oral health; pi 1i is the slope of an individual's oral health as a function of age; pi 2i is the expected value of oral health change corresponding to an increase in income (log value); pi 3i represents the expected value of the slope of the oral health change caused by the interactive variables of income and age; eti is the residual error of a particular individual i at time t, following a normal distribution with a mean of 0 and a variance of σ. Other time-varying control variables Xj were placed in the first level model, including the marital status of each measurement and whether or not to die in the next follow-up survey. The first layer model mainly measures the change track of the health of an individual with age. In order to measure the heterogeneity of individual health track changes and detect the influence of gender and socioeconomic characteristics of individual level on the individual health change track, the influence of individual characteristics on individual intercept and slope parameters in a first-level model is measured. The second layer model contains the following series of equations. The second layer model:

the second layer model comprises 4 formulas, wherein the formula (2) measures an intercept parameter pi 0i in the first layer model, and the formulas (3), (4) and (5) respectively measure slope parameters pi 1i, pi 2i and pi 3i in the first layer model. The parameter β pq is a fixed effect model parameter and represents the influence of individual characteristics such as gender, education and the like on intercept and slope parameters in the first-layer model. β 00- β 03 are parameters of the intercept model π 0i, measuring the influence of gender, education, and interactive variables of gender and education on the intercept. Other age-invariant individual-level control variables Zj, such as generation, household, and region, are also included in the intercept parameter model. β 10- β 13 is the pi 1i parameter of the above gender, education, "gender x education" variables on the healthy growth slope, and the interactive effect of these variables on age. β 20 and β 21 measure the parameters of interaction effect of gender with household income (covariate of time π 2 i), while β 30 and β 31 measure the parameters of three-dimensional interaction effect of gender, income with age π 3 i. γ 10 and γ 1i are random effects of intercept and first order slope, and also follow a normal distribution with a mean of 0. Together, γ 10, γ 1i and eti in equation (1) make up the variance of the random effect. For the study hypothesis, β 03 and β 20 are parameters of validation hypothesis 1, β 03 and π 31 are parameters of validation hypothesis 2, and β 12, β 13, β 30 and β 31 validate hypothesis 3.

Through an electronic data acquisition system, the influence of the socioeconomic status and the social gender on the oral health transition of an individual in the life history is tracked by using the tracking data of the cross-year. Therefore, whether the social economic factors return oral health of men and women consistently in life history or not is known, and whether the influence is increased with the lapse of time or not is known.

The early socio-economic, psycho-social and behavioral factors of an individual are analyzed to predict the later oral health conditions, such as caries, periodontal disease, tooth loss, tooth trauma, open and close teeth, tooth eruption and even tooth pain.

Preferably, the basic statistic calculation includes a mean and a ratio.

Further, the examination questionnaire 4 is used for recording personal information, oral examinations and oral health KAP surveys of the respondents in the oral health epidemiological survey field, wherein the personal information of the respondents comprises an ID number, a name, a gender, a ethnicity, a family type, an occupation, an education age, a birth date and an age; the financial management 5 is used for filling in contents such as medical equipment application, consumables and the like; the track management unit 7 is used for uploading a photo 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 9 is used for the investigators to fill in the work plan summary and the log of the investigators; the answered management 10 is used for checking the inspection list and questionnaire which are not uploaded by the investigator, and checking; and the offline management 11 is used for managing the data entered by the investigator in the offline environment, and selecting and uploading the data to the storage transfer station 2 in the WiFi environment.

Preferably, the storage transfer station 2 is a central server, the data processing workstation 1 is a background management computer, the data entry workstation 3 comprises a handheld tablet computer device end or a mobile phone, the data entry workstation 3 comprises a handheld tablet computer or a mobile phone, and the configuration of the handheld tablet computer or the mobile phone is that the system is an android 7.0 system, 32GB and above storage capacity, 3GB and above memory, and a GPS positioning indicator. The invention installs the software into the hand-held tablet computer or the mobile phone, and takes the hand-held tablet computer as the data entry workstation 3.

Claims

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.