CN109346173A - The system and method for realization health life expectancy in life expectancy operational analysis function based on hygiene medical treatment big data - Google Patents

The system and method for realization health life expectancy in life expectancy operational analysis function based on hygiene medical treatment big data Download PDF

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CN109346173A
CN109346173A CN201811234485.5A CN201811234485A CN109346173A CN 109346173 A CN109346173 A CN 109346173A CN 201811234485 A CN201811234485 A CN 201811234485A CN 109346173 A CN109346173 A CN 109346173A
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life expectancy
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王春芳
虞慧婷
蔡任之
夏天
付晨
方博
陈蕾
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SHANGHAI DISEASE PREVENTION AND CONTROL CENTRE
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Abstract

The system and method for the realization health life expectancy in life expectancy operational analysis function based on hygiene medical treatment big data that the present invention relates to a kind of, including hygiene medical treatment big data subsystem, for collecting and integrating hygiene medical treatment data；Health life expectancy in life expectancy analytical database management subsystem, the data base management subsystem of measuring and calculating health life expectancy in life expectancy is established for the hygiene medical treatment data after collecting and integrate according to the hygiene medical treatment big data subsystem, the health life expectancy in life expectancy analytical database management subsystem is connected with the hygiene medical treatment big data subsystem；Health life expectancy in life expectancy calculates analysis model subsystem, and for calculating health life expectancy in life expectancy, the health life expectancy in life expectancy measuring and calculating analysis model subsystem is connected with the health life expectancy in life expectancy analytical database management subsystem.Using the system and method for the realization health life expectancy in life expectancy operational analysis function based on hygiene medical treatment big data, unified, standardized automatic measurement & calculation and intellectual analysis are provided for the measuring and calculating of Shanghai City health life expectancy in life expectancy.

Description

Realization health life expectancy in life expectancy operational analysis function based on hygiene medical treatment big data is System and method
Technical field
The present invention relates to a kind of medical analysis systems more particularly to a kind of health life expectancy in life expectancy methods, in particular to one kind The system and method for realization health life expectancy in life expectancy operational analysis function based on hygiene medical treatment big data.
Background technique
Population health status evaluation is the main social hygiene's problem of discovery, finds focused protection crowd and keypoint control pair As evaluating the main path of Disease Intervention measure effect, and science configuration Health Services and health resources, the best disease of selection The important evidence of control program.
Life expectancy (LE, Life expectancy) is to measure one of the representative index of population health situation, is being evaluated Important function is played in Shanghai popualtion healthy level and Health Services effect, but the index can only embody service life length, it cannot Reflection serving span comprehensively, reduces the sensibility of population health situation increasingly.Health life expectancy in life expectancy (HALE, health- Adjusted life expectancy) can the length of concentrated expression life and the quality of life, be on the basis of Life Table On, a series of indexs such as the health status of crowd, functional status, mobility and death state are combined, overall merit The health status of crowd.Health life expectancy in life expectancy has been classified as the composite target of evaluation countries in the world population health situation by WHO.
Although the concept of health life expectancy in life expectancy is explicitly, since health is a Multidimensional Concept in itself, to need multidimensional Degree carries out population health measurement or fitness assessment, not only needs to obtain complete crowd's death data, it is also necessary to full and accurate crowd Disease incidence, illness rate, disability, disability data, and the objective investigation to population health situation.The data type not only needed is numerous More, high number, and necessarily refer to the analysis method of big data.
Although the research of most domestic health life expectancy in life expectancy is in terms of to crowd's comprehensive health assessment compared with life expectancy at present It is greatly improved, but still has biggish limitation, specific as follows:
(1) data source is more single, can only reflect the health status of some or certain several dimensions
Causes of Death Surveillance data, Self-report Health Survey are mainly applied in the correlative study of most domestic health life expectancy in life expectancy The monitoring data of data and certain class disease carry out the measuring and calculating of health life expectancy in life expectancy, and data source is more single.And crowd is true Healthy mass data, if without powerful database as support, cannot achieve mass data summarize, clean, assessing and Analysis.
(2) it has a single function, is unable to the true health status of comprehensive assessment crowd
Resident's subjectivity health status is mainly assessed in the research of most domestic health life expectancy in life expectancy, or without certain mistake The health life expectancy in life expectancy of energy, or the health life expectancy in life expectancy without certain class disease, function is simple, and it is also more single to be formed by index One.Health impact caused by various disease can neither be evaluated, can not quantitatively calculate the risk factors such as smoke, drink to crowd It is endangered caused by health, is unable to the true health status of Comprehensive reflection crowd.
(3) professional ability of dependency analysis personnel
Health life expectancy in life expectancy measuring and calculating and analysis be professional very strong work, especially to public health requested knowledge compared with Height will carry out health life expectancy in life expectancy measuring and calculating and analysis for the personnel or team in data management and statistical analysis scarce capacity It is a very difficult task.Existing health life expectancy in life expectancy analytical model is too dependent on the ability of professional, in weight The effective measuring and calculating that also not necessarily can be realized sustainable health life expectancy in life expectancy is expended on the basis of a large amount of manpower and material resources again.
Summary of the invention
The purpose of the present invention is overcoming the above-mentioned prior art, providing a kind of can integrate various diseases monitoring number According to, carry out data multidimensional analysis and excavation, establish can face reflection population health situation serving span it is big based on hygiene medical treatment The system and method for the realization health life expectancy in life expectancy operational analysis function of data.
To achieve the goals above, the realization health life expectancy in life expectancy operational analysis of the invention based on hygiene medical treatment big data The system and method for function is as follows:
The system of the realization health life expectancy in life expectancy operational analysis function based on hygiene medical treatment big data, main feature It is that the system includes:
Hygiene medical treatment big data subsystem, for collecting and integrating hygiene medical treatment data；
Health life expectancy in life expectancy analytical database management subsystem, for according to the hygiene medical treatment big data subsystem receipts Hygiene medical treatment data after collecting and integrating establish the data base management subsystem of measuring and calculating health life expectancy in life expectancy, the Health needs Durability analysis data base management subsystem is connected with the hygiene medical treatment big data subsystem；
Health life expectancy in life expectancy calculates analysis model subsystem, for calculating health life expectancy in life expectancy, the Health needs longevity Life measuring and calculating analysis model subsystem is connected with the health life expectancy in life expectancy analytical database management subsystem.
Preferably, the hygiene medical treatment big data subsystem includes:
Hygiene medical treatment data collection module, for collecting hygiene medical treatment data；
Multi-source magnanimity isomeric data integration module, it is described for being encoded to the hygiene medical treatment data after collection Multi-source magnanimity isomeric data integration module is connected with the hygiene medical treatment data collection module.
Preferably, the health life expectancy in life expectancy analytical database management subsystem includes:
Health life expectancy in life expectancy classification of diseases special topic knowledge data database management module, with the hygiene medical treatment big data subsystem System is connected, for calculating the joint data of management various disease and its sick disability weight；
Health life expectancy in life expectancy calculates disability weighted data database management module, special with the health life expectancy in life expectancy classification of diseases Topic knowledge data database management module is connected, and manages the concurrent healthy weighted data of multiple diseases for calculating.
Preferably, the health life expectancy in life expectancy measuring and calculating analysis model subsystem includes sick disability adjustment expectation durability analysis Module and self-appraisal health life expectancy in life expectancy analysis module, the sick disability adjustment expectation durability analysis module and self-appraisal Health needs Durability analysis module is connected with the health life expectancy in life expectancy analytical database management subsystem.
Preferably, the sick disability adjustment expectation durability analysis module includes each age group crowd Weight Measurement submodule Health impact caused by block, health life expectancy in life expectancy computational submodule and sick disability measures submodule, each age group crowd power It resurveys health impact measurement submodule caused by operator module, health life expectancy in life expectancy computational submodule and sick disability and is sequentially connected and connect.
Preferably, each age group crowd Weight Measurement submodule analyzes data by the health life expectancy in life expectancy Data in the management subsystem of library calculate the disability weight of each age group.
Preferably, the health life expectancy in life expectancy computational submodule includes life expectancy measuring and calculating unit and health life expectancy in life expectancy Calculation using models unit, the life expectancy measuring and calculating unit are connected with each age group crowd Weight Measurement submodule, The health life expectancy in life expectancy calculation using models unit is connected with the life expectancy measuring and calculating unit.
Preferably, the measurement submodule of health impact caused by the sick disability includes that the lethal life loss of different reasons is surveyed The life years loss measuring and calculating unit that calculation unit and different reasons are caused injury, the loss of the different reasons lethal service life year is calculated single The life years loss measuring and calculating unit that first and different reasons are caused injury is connected with the health life expectancy in life expectancy computational submodule.
Preferably, the self-appraisal health life expectancy in life expectancy analysis module includes self-appraisal health survey submodule and self-report health Correction module is calculated, the self-appraisal health survey submodule and the health life expectancy in life expectancy analytical database manage subsystem System is connected, and the self-report health calculates correction module and is connected with the self-appraisal health survey submodule.
Include self-report health data pre-calculation unit, report and be good for certainly preferably, the self-report health calculates correction module Health data correction unit and self-report health Data Computation Unit, the self-report health data pre-calculation unit, self-report health number It is sequentially connected according to correction unit and self-report health Data Computation Unit, the self-report health data pre-calculation unit and described Self-appraisal health survey submodule is connected.
Preferably, the self-report health data pre-calculation unit includes self-report health part precomputation subelement and health Scene part precomputation subelement, the self-report health part precomputation subelement and healthy scene part precomputation subelement It is connected with the self-appraisal health survey submodule.
This is based on hygiene medical treatment big data using above system and realizes health life expectancy in life expectancy operational analysis control method, master Wanting feature is, method includes the following steps:
(1) the hygiene medical treatment big data subsystem described in collects the hygiene medical treatment data of different crowd and the number to multi-source According to being integrated；
(2) the health life expectancy in life expectancy analytical database management subsystem described in is to the hygiene medical treatment big data subsystem Data after collecting integration establish database；
(3) the health life expectancy in life expectancy measuring and calculating analysis model subsystem described in analyzes data according to the health life expectancy in life expectancy Data in the management subsystem of library calculate health life expectancy in life expectancy, and show the health life expectancy in life expectancy；
Preferably, the step (1) the following steps are included:
(1.1) the hygiene medical treatment big data subsystem described in collects the hygiene medical treatment data of different crowd；
(1.2) the hygiene medical treatment big data subsystem described in integrates the isomeric data of multi-source.
Preferably, the hygiene medical treatment data that the hygiene medical treatment big data subsystem is collected include disease surveillance data, Crowd's self-appraisal health data, resident's diagnosis and treatment data and group of handicapped's registration data.
Preferably, the step (1.2) the following steps are included:
Hygiene medical treatment big data subsystem described in (1.2.1) encodes the isomery number to multi-source according to International Classification of Diseases Disease data in is encoded；
Hygiene medical treatment big data subsystem described in (1.2.2) fills up missing data according to different data feature；
Hygiene medical treatment big data subsystem described in (1.2.3) carries out heterogeneous analysis to the multi-source data of same disease And carry out aggregation of data.
Preferably, the step (2) the following steps are included:
(2.1) health life expectancy in life expectancy analytical database management subsystem building various disease and its sick disability weight described in The database of joint data；
(2.2) the health life expectancy in life expectancy analytical database management subsystem described in calculates the concurrent right of health of multiple diseases Weight, and construct disability weight database.
Preferably, the healthy weight of the multiple diseases of measuring and calculating concurrently in the step (2.2), specifically:
The concurrent healthy weight of multiple diseases is calculated according to the following formula:
Wherein, n is kinds of Diseases, ωi(i=1 ... n) is the healthy weight of every kind of disease.
Preferably, the health life expectancy in life expectancy measuring and calculating analysis model subsystem includes sick disability adjustment expectation durability analysis Module, the measuring and calculating disease disability in the step (3) adjust life expectancy, specifically includes the following steps:
Sick disability adjustment expectation durability analysis module described in (3-1.1) aggregates out each age group crowd disease, hurts, is residual The prevalence rate of state and corresponding disability weight, and calculate the disability weight of each age group；
Sick disability adjustment expectation durability analysis module described in (3-1.2) calculates life expectancy and health life expectancy in life expectancy；
Sick disability described in (3-1.3) adjusts the health impact caused by expectation durability analysis module is calculated because of sick disability Value.
Preferably, the disability weight of each age group of calculating in the step (3-1.1), specifically:
The disability weight of each age group is calculated according to the following formula:
Wherein, i (i=1 ... k) is disease/disability type, and k is that the type of disease, disability or merging disease disability is total Number, j (j=1 ... 85+) are age group.
Preferably, the step (3-1.2) the following steps are included:
Sick disability adjustment expectation durability analysis module described in (3-1.2.1) calculates life expectancy；
Sick disability adjustment expectation durability analysis module described in (3-1.2.2) calculates health according to the life expectancy Life expectancy and corresponding health impact service life year.
Preferably, the calculating life expectancy in the step (3-1.2.1), specifically:
Life expectancy is calculated according to the following formula:
Wherein, TxTotal person-time for survival, lxIt survives when being x years old number, exThe life expectancy of crowd when being x years old.
Preferably, the calculating health life expectancy in life expectancy in the step (3-1.2.2), specifically:
Health life expectancy in life expectancy is calculated according to the following formula:
Wherein, YWDxFor the healthy life span between x years old to x+5 years old, ω indicates the highest age group in Life Table, lx It survives number when indicating x years old, HALExThe health life expectancy in life expectancy of crowd at as x years old.
Preferably, the calculating health impact service life year in the step (3-1.2.2), specifically:
The health impact service life year is calculated according to the following formula:
Wherein, YDxFor the disability life span between x years old to x+5 years old, LExThe life expectancy of crowd, HALE when being x years oldx For health life expectancy in life expectancy, LHExThe as health impact service life year.
Preferably, because the health impact value caused by sick disability includes that different reasons are lethal in the step (3-1.3) The life years costing bio disturbance processing that service life year costing bio disturbance processing and different reasons are caused injury, the different reasons lethal service life Year costing bio disturbance processing, specifically:
Penalty values YLL of different reasons lethal service life year is calculated according to the following formula:
YLL=NCe(ra)/(β+r)2[e-(β+r)(L+α)[-(β+r)(L+α)-1]-e-(β+r)α[- (β+r) α -1]],
Wherein, L is the age group mean age at death calculated by standard life expectancy, and N is the death toll of the specific cause of disease, R is discount rate, and C is that age weight corrects constant, and β is age weight parameter, and a is age of onset；
The life years costing bio disturbance processing that the different reasons are caused injury, specifically:
The life years penalty values YLD that different reasons are caused injury is calculated according to the following formula:
YLD=IDWCe(rα)/(β+r)2[e-(β+r)(L+α)[-(β+r)(L+α)-1]-e-(β+r)α[- (β+r) α -1]],
Wherein, I is the morbidity number of cases of each disease, and DW is the corresponding disability weight of various diseases, and L is the course of disease, and r is discount Rate, C are that age weight corrects constant, and β is age weight parameter, and a is age of onset.
Preferably, the health life expectancy in life expectancy measuring and calculating analysis model subsystem includes self-appraisal health life expectancy in life expectancy analysis mould Block, the measuring and calculating self-appraisal health life expectancy in life expectancy in the step (3), specifically includes the following steps:
Self-appraisal health life expectancy in life expectancy analysis module described in (3-2.1) collects the self-appraisal health survey of each crowd；
Self-appraisal health life expectancy in life expectancy analysis module described in (3-2.2) calculates self-report health data, and according to crowd's self-appraisal Right of health log-log evidence is corrected calculating.
Preferably, the self-appraisal health survey of each crowd includes self-report health part and scene in the step (3-2.1) Description section.
Preferably, the step (3-2.2), comprising the following steps:
The health data and feelings of the calculating of self-appraisal health life expectancy in life expectancy analysis module described in (3-2.2.1) self-report health part The health data of scene describing part；
Self-appraisal health life expectancy in life expectancy analysis module described in (3-2.2.2) identifies and corrects point of contact；
Self-appraisal health life expectancy in life expectancy analysis module described in (3-2.2.3) calculates crowd's self-appraisal health weight；
Self-appraisal health life expectancy in life expectancy analysis module described in (3-2.2.4) calculates self-appraisal Health needs according to Sullivan method Service life.
Preferably, the health data of self-report health part is calculated in the step (3-2.2.1), specifically:
The health data y of self-report health part is calculated according to the following formulai *Y:
yi *=xiβ+εi；
Wherein, xiFor covariant, β is fixed effect, εiFor residual error item.
Preferably, the health data of the computation scenarios description section in the step (3-2.2.1), specifically:
The health data z of scene description section is calculated according to the following formulaij:
Wherein, θjIt is respondent i to the true health of the imaginary personage of j-th of healthy scene description.
Preferably, calculating crowd's self-appraisal health weight in the step (3-2.2.3), specifically:
Crowd's self-appraisal health weight is calculated according to the following formula:
Wherein, y2Estimate to adjust to the disability behind [0,1] section, y1Crowd's self-report health is corrected for Chopit model Crowd's disability score afterwards, ymaxAnd yminRespectively represent minimum and maximum score.
What it is using the realization health life expectancy in life expectancy operational analysis function based on hygiene medical treatment big data in the invention is System and method provide unified, standardized automatic measurement & calculation and intellectual analysis for the measuring and calculating of Shanghai City health life expectancy in life expectancy.
(1) health life expectancy in life expectancy data resource management platform.The building of knowledge type data warehouse: by literature search, specially Family's consulting and web crawlers, convergence health life expectancy in life expectancy calculate relevant knowledge data, take into account that data query utilizes convenient and The expansion of information analysis dimension, the star-like data bins of knowledge of the information such as building collection classification of diseases system, healthy weight, risk factor Library, the sustainability for health life expectancy in life expectancy analysis lay the foundation；The building of analytic type data warehouse: long-acting smooth number is relied on It is the extraction of data resource and historical data needed for realizing health life expectancy in life expectancy, cleaning, fractionation, reconstruct, whole according to collecting channel It closes, and is arranged, integrated by information resources data framework, data standard specification etc., and integrated according to unified data model Landing, formation is sought unity of standard, unification indexes, so that medical treatment relevant to health life expectancy in life expectancy, hygiene information resources progress are effectively Grasp and management, for health care information resources in-depth using laying the foundation.Major function includes: source data management, letter Cease inventory management of resources, information resources Mapping and Converting, data intelligence integration, data exchange standard management etc..
(2) health life expectancy in life expectancy data application service platform: the health care that data-oriented resource management platform has been integrated Information resources are calculated according to health life expectancy in life expectancy and analyze demand to data progress taxonomic revision, and establish index directory index, To realize the shared of data resource and utilize.In addition to this, health life expectancy in life expectancy data application service platform combination Shanghai City is strong The existing Quality Control Mechanism of health Information Network is formed for big data using middle finger target, application process carries out quality of data prison It surveys, assessment, cleaning, improves index application confidence level.Major function includes: operating mechanism and standardized administration, data quality accessment And verification, operation maintenance management, main body management.
(3) health life expectancy in life expectancy big data specific analysis platform: all kinds of business collected based on data resource management platform The population health that data and sample investigation obtain assesses data, by statistical model and big data analysis technology, completes a system Column health life expectancy in life expectancy index system, realizing has effect to Shanghai City health care big data based on health life expectancy in life expectancy theme With.Major function includes: data management and maintenance, sample investigation management, the management of health life expectancy in life expectancy index system, Health needs (life expectancy, disease-free life expectancy are adjusted without disability life expectancy, self-appraisal health life expectancy in life expectancy, sick disability for service life specific analysis Life expectancy), comprehensive analysis etc..Specific module has: data correlation and maintenance: according to data quality accessment result to data exchange And incidence relation involved in resource impact carries out periodic maintenance；It is formed according to data using demand and existing data correlation is closed System carries out the maintenance that additions and deletions change etc.；Sample investigation management: it realizes to the Sampling Frame management of sample investigation, sampling of data, sample database The functions such as management, survey item management.Sampling Frame includes the list or register of all target samples, can be checked existing all The list of Sampling Frame can carry out data addition, deletion, modification, batch importing/export etc..It can be according to required by different investigation Sample range, sample size and the methods of sampling, obtain sample data.Realize the Classification Management of Different categories of samples, detail are safeguarded, The functions such as analyses and comparison.Unified storage and management are carried out to survey item, record the project name of all previous investigation, responsible department, Survey objective, etc. essential informations, realize data addition, deletions, modification, batch importing/export, filter inquire etc. functions, mainly It is to play filing and management role after the completion of sample investigation project.Health life expectancy in life expectancy specific analysis: in information resource platform On the basis of, it realizes that abridged life table and complete Life Table calculate step, the comprehensive analysis of life expectancy is completed, in conjunction with the healthy phase It hopes the knowledge data base of lifetime data resource management platform, realizes sick disability and risk factor measuring and calculating, construct sick disability adjustment period It hopes service life specific analysis modeling tool, completes sick disability and adjust life expectancy specific analysis.Based on population health sample investigation number According to Chopit model to the general health and pain of different regions and age groups crowd, motor function, mood The general level of the health of 8 dimensions such as anxiety is corrected respectively, realizes the health evaluating based on self-appraisal data, and application of results according to this Sullivan method calculates resident's self-appraisal health life expectancy in life expectancy.
(4) health life expectancy in life expectancy big data space-time shows platform.By arranging from time and Spatial Dimension, analysis shows respectively Area health life expectancy index of correlation, and pass through the analysis methods such as space-time cross correlation, fluoro data potential value information, hair It now hides rule and off-note, the principal disease and risk factor of analyzing influence Shanghai Residents Health needs mentions for decision For foundation.Major function includes: based on spatio-temporal multidimensional data analysis, health life expectancy in life expectancy report management etc..
Detailed description of the invention
Fig. 1 is the system of the realization health life expectancy in life expectancy operational analysis function of the invention based on hygiene medical treatment big data General illustration.
Fig. 2 is the system of the realization health life expectancy in life expectancy operational analysis function of the invention based on hygiene medical treatment big data Concrete structure schematic diagram.
Fig. 3 is the method for the realization health life expectancy in life expectancy operational analysis function of the invention based on hygiene medical treatment big data Overview flow chart.
Fig. 4 is the method for the realization health life expectancy in life expectancy operational analysis function of the invention based on hygiene medical treatment big data Specific process flow diagram.
Specific embodiment
It is further to carry out combined with specific embodiments below in order to more clearly describe technology contents of the invention Description.
In a specific embodiment of the invention, as shown in Figure 1, should the realization Health needs longevity based on hygiene medical treatment big data The system for ordering operational analysis function, comprising:
(1) hygiene medical treatment big data subsystem, for collecting and integrating hygiene medical treatment data；
(2) health life expectancy in life expectancy analytical database management subsystem, for according to the hygiene medical treatment big data subsystem Hygiene medical treatment data after system is collected and integrated establish the data base management subsystem for calculating health life expectancy in life expectancy, the health Life expectancy analytical database management subsystem is connected with the hygiene medical treatment big data subsystem；
(3) health life expectancy in life expectancy calculates analysis model subsystem, for calculating health life expectancy in life expectancy, the Health needs Service life measuring and calculating analysis model subsystem is connected with the health life expectancy in life expectancy analytical database management subsystem.
As shown in Figure 2, wherein the hygiene medical treatment big data subsystem includes:
Hygiene medical treatment data collection module, for collecting hygiene medical treatment data；
Multi-source magnanimity isomeric data integration module, it is described for being encoded to the hygiene medical treatment data after collection Multi-source magnanimity isomeric data integration module is connected with the hygiene medical treatment data collection module.
The health life expectancy in life expectancy analytical database management subsystem includes:
Health life expectancy in life expectancy classification of diseases special topic knowledge data database management module, with the hygiene medical treatment big data subsystem System is connected, for calculating the joint data of management various disease and its sick disability weight；
Health life expectancy in life expectancy calculates disability weighted data database management module, special with the health life expectancy in life expectancy classification of diseases Topic knowledge data database management module is connected, and manages the concurrent healthy weighted data of multiple diseases for calculating.
The described health life expectancy in life expectancy measuring and calculating analysis model subsystem include sick disability adjustment expectation durability analysis module and Self-appraisal health life expectancy in life expectancy analysis module, the sick disability adjustment expectation durability analysis module and self-appraisal health life expectancy in life expectancy point Analysis module is connected with the health life expectancy in life expectancy analytical database management subsystem.
The sick disability adjustment expectation durability analysis module includes each age group crowd Weight Measurement submodule, healthy phase Health impact caused by service life computational submodule and sick disability is hoped to measure submodule, each age group crowd Weight Measurement submodule Health impact measurement submodule, which is sequentially connected, caused by block, health life expectancy in life expectancy computational submodule and sick disability connects.
Each age group crowd Weight Measurement submodule passes through the health life expectancy in life expectancy analytical database management Data in subsystem calculate the disability weight of each age group.
The health life expectancy in life expectancy computational submodule includes that life expectancy measuring and calculating unit and health life expectancy in life expectancy model are surveyed Unit is calculated, the life expectancy measuring and calculating unit is connected with each age group crowd Weight Measurement submodule, described Health life expectancy in life expectancy calculation using models unit is connected with the life expectancy measuring and calculating unit.
The measurement submodule of health impact caused by the sick disability includes the lethal life loss measuring and calculating unit of different reasons The life years loss measuring and calculating unit caused injury with different reasons, loss of the different reasons lethal service life year measuring and calculating unit and not It is connected with the health life expectancy in life expectancy computational submodule with the life years loss measuring and calculating unit that reason is caused injury.
The self-appraisal health life expectancy in life expectancy analysis module includes that self-appraisal health survey submodule and self-report health calculate school Syndrome generation module, the self-appraisal health survey submodule are connected with the health life expectancy in life expectancy analytical database management subsystem It connects, the self-report health calculates correction module and is connected with the self-appraisal health survey submodule.
It includes self-report health data pre-calculation unit, self-report health data school that the self-report health, which calculates correction module, Positive unit and self-report health Data Computation Unit, the self-report health data pre-calculation unit, self-report health Data correction list Member and self-report health Data Computation Unit are sequentially connected, the self-report health data pre-calculation unit and self-appraisal health Investigation submodule is connected.
The self-report health data pre-calculation unit includes self-report health part precomputation subelement and healthy scene portion Point precomputation subelement, the self-report health part precomputation subelement and health scene part precomputation subelement are and institute The self-appraisal health survey submodule stated is connected.
In a specific embodiment of the invention, this is based on hygiene medical treatment big data using above system and realizes Health needs Service life operational analysis control method, as shown in Figure 3 and Figure 4, including following steps:
(1) the hygiene medical treatment big data subsystem described in collects the hygiene medical treatment data of different crowd and the number to multi-source According to being integrated；
(1.1) the hygiene medical treatment big data subsystem described in collects the hygiene medical treatment data of different crowd；
(1.2) the hygiene medical treatment big data subsystem described in integrates the isomeric data of multi-source；
Hygiene medical treatment big data subsystem described in (1.2.1) encodes the isomery number to multi-source according to International Classification of Diseases Disease data in is encoded；
Hygiene medical treatment big data subsystem described in (1.2.2) fills up missing data according to different data feature；
Hygiene medical treatment big data subsystem described in (1.2.3) carries out heterogeneous analysis to the multi-source data of same disease And carry out aggregation of data.
(2) the health life expectancy in life expectancy analytical database management subsystem described in is to the hygiene medical treatment big data subsystem Data after collecting integration establish database；
(2.1) health life expectancy in life expectancy analytical database management subsystem building various disease and its sick disability weight described in The database of joint data；
(2.2) the health life expectancy in life expectancy analytical database management subsystem described in calculates the concurrent right of health of multiple diseases Weight, and construct disability weight database.
(3) the health life expectancy in life expectancy measuring and calculating analysis model subsystem described in analyzes data according to the health life expectancy in life expectancy Data in the management subsystem of library calculate health life expectancy in life expectancy, and show the health life expectancy in life expectancy；
Sick disability adjustment expectation durability analysis module described in (3-1.1) aggregates out each age group crowd disease, hurts, is residual The prevalence rate of state and corresponding disability weight, and calculate the disability weight of each age group；
Sick disability adjustment expectation durability analysis module described in (3-1.2) calculates life expectancy and health life expectancy in life expectancy；
Sick disability adjustment expectation durability analysis module described in (3-1.2.1) calculates life expectancy；
Sick disability adjustment expectation durability analysis module described in (3-1.2.2) calculates health according to the life expectancy Life expectancy and corresponding health impact service life year.
Sick disability described in (3-1.3) adjusts the health impact caused by expectation durability analysis module is calculated because of sick disability Value.
Self-appraisal health life expectancy in life expectancy analysis module described in (3-2.1) collects the self-appraisal health survey of each crowd；
Self-appraisal health life expectancy in life expectancy analysis module described in (3-2.2) calculates self-report health data, and according to crowd's self-appraisal Right of health log-log evidence is corrected calculating.
The health data and feelings of the calculating of self-appraisal health life expectancy in life expectancy analysis module described in (3-2.2.1) self-report health part The health data of scene describing part；
Self-appraisal health life expectancy in life expectancy analysis module described in (3-2.2.2) identifies and corrects point of contact；
Self-appraisal health life expectancy in life expectancy analysis module described in (3-2.2.3) calculates crowd's self-appraisal health weight；
Self-appraisal health life expectancy in life expectancy analysis module described in (3-2.2.4) calculates self-appraisal Health needs according to Sullivan method Service life.
The hygiene medical treatment data that the hygiene medical treatment big data subsystem is collected include disease surveillance data, crowd's self-appraisal Health data, resident's diagnosis and treatment data and group of handicapped's registration data.
The healthy weight of the multiple diseases of measuring and calculating concurrently in the step (2.2), specifically:
The concurrent healthy weight of multiple diseases is calculated according to the following formula:
Wherein, n is kinds of Diseases, ωi(i=1 ... n) is the healthy weight of every kind of disease.
The disability weight of each age group of calculating in the step (3-1.1), specifically:
The disability weight of each age group is calculated according to the following formula:
Wherein, i (i=1 ... k) is disease/disability type, and k is that the type of disease, disability or merging disease disability is total Number, j (j=1 ... 85+) are age group.
Calculating life expectancy in the step (3-1.2.1), specifically:
Life expectancy is calculated according to the following formula:
Wherein, TxTotal person-time for survival, lxIt survives number when indicating x years old, exThe life expectancy of crowd at as x years old.
Calculating health life expectancy in life expectancy in the step (3-1.2.2), specifically:
Health life expectancy in life expectancy is calculated according to the following formula:
Wherein, YWDxFor the healthy life span between x years old to x+5 years old, ω indicates the highest age group in Life Table, lx It survives number when indicating x years old, HALExThe health life expectancy in life expectancy of crowd at as x years old.
The calculating health impact service life year in the step (3-1.2.2), specifically:
The health impact service life year is calculated according to the following formula:
Wherein, YDxFor the disability life span between x years old to x+5 years old, LExThe life expectancy of crowd, HALE when being x years oldx For health life expectancy in life expectancy, LHExThe as health impact service life year.
Because the health impact value caused by sick disability includes the lethal service life annual loss of different reasons in the step (3-1.3) The life years costing bio disturbance processing that mistake calculation processing and different reasons are caused injury, the lethal service life annual loss of the different reasons are unwise Calculation processing, specifically:
Penalty values YLL of different reasons lethal service life year is calculated according to the following formula:
YLL=NCe(ra)/(β+r)2[e-(β+r)(L+α)[-(β+r)(L+α)-1]-e-(β+r)α[- (β+r) α -1]],
Wherein, L is the age group mean age at death calculated by standard life expectancy, and N is the death toll of the specific cause of disease, R is discount rate, and C is that age weight corrects constant, and β is age weight parameter, and a is age of onset；
The life years costing bio disturbance processing that the different reasons are caused injury, specifically:
The life years penalty values YLD that different reasons are caused injury is calculated according to the following formula:
YLD=IDWCe(rα)/(β+r)2[e-(β+r)(L+α)[-(β+r)(L+α)-1]-e-(β+r)α[- (β+r) α -1]],
Wherein, I is the morbidity number of cases of each disease, and DW is the corresponding disability weight of various diseases, and L is the course of disease, and r is discount Rate, C are that age weight corrects constant, and β is age weight parameter, and a is age of onset.
The health data of self-report health part is calculated in the step (3-2.2.1), specifically:
The health data y of self-report health part is calculated according to the following formulai *Y:
yi *=xiβ+εi；
Wherein, xiFor covariant, β is fixed effect, εiFor residual error item.
The health data of computation scenarios description section in the step (3-2.2.1), specifically:
The health data z of scene description section is calculated according to the following formulaij:
Wherein, θjIt is respondent i to the true health of the imaginary personage of j-th of healthy scene description.
Calculating crowd's self-appraisal health weight in the step (3-2.2.3), specifically:
Crowd's self-appraisal health weight is calculated according to the following formula:
Wherein, y2Estimate to adjust to the disability behind [0,1] section, y1Crowd's self-report health is corrected for Chopit model Crowd's disability score afterwards, ymaxAnd yminRespectively represent minimum and maximum score.
In actual use, the present invention relates to cross-platform intelligence integration medical treatment, health and population healths to measure big data, In conjunction with the requirement of health life expectancy in life expectancy model construction, verified by logic to magnanimity isomeric data, missing verification, Noise reducing of data, The programs such as data encoding standards establish intelligent data analysis platform, form continuous, complete, believable crowd's multidimensional health and fitness information Data, and the integrality to data, accuracy, uniqueness and logical relation carry out overall merit, to find that platform docks in time The problem of in the presence of data, simultaneously feeds back to source system, promotes the common promotion of the quality of data.On this basis, using high concurrent Processing, High Availabitity processing, cluster, real-time calculate etc. big datas technology, in conjunction with multiple imputation, multistage orderly latent variable model and The Statistic analysis models such as Sullivan method, establish the analysis model of health life expectancy in life expectancy measuring and calculating and its influence factor, and realization is based on The health life expectancy in life expectancy application support information system of big data.
The present invention is the primary innovation of public health, clinic diagnosis and the cross-platform comprehensive utilization of population health big data, is filled out Blank of the whole nation based on the true health of full crowd, health and diagnosis and treatment data measuring and calculating health life expectancy in life expectancy has been mended, logarithm is formed by According to automation, intelligent management mode and cured model analysis mode be the long-term monitoring and assessment for carrying out residents ' health, Timely and effective analysis threatens the principal disease or risk factor of population health, and to find reasonable intervening measure, it is strong to improve crowd Kang Shuiping provides strong information and foundation, provides continuous, complete and scientific evidence-based foundation for the formulation of health policy.
Emphasis of the present invention is directed under the conditions of existing information, and by information-based approach, cross-platform integration magnanimity crowd is strong Health data, the long-acting collection more new channel of data needed for constructing health life expectancy in life expectancy form continuous, complete, believable data volume System；Data resource catalogue needed for health life expectancy in life expectancy is established, on this basis to the birth death data of collection, clinic diagnosis number Classified according to, disease, injury and risk factor monitoring data, cleaned, markization, fractionation, reconstruct, it is special to form multi-class data Inscribe warehouse complex；Based on health life expectancy in life expectancy model, by data special topic warehouse complex and and ad hoc survey data massive information, It is calculated by mass data specific analysis, realizes the space-time analysis of various dimensions and show；Analyze principal disease and its risk factor Influence to population health, early warning risk factor and disease provide foundation for the formulation of health policy and the implementation of precautionary measures, The comprehensive utilization ratio for mentioning sanitization medical data proposes the efficiency of raw hygienic Evidence-based decision.
The present invention is the limitation for solving life expectancy on health assessment, overcomes existing health life expectancy in life expectancy measuring and calculating existing At least one defect described in technology develops a kind of health life expectancy in life expectancy operational analysis mould based on hygiene medical treatment big data Type integrates examining for various diseases monitoring data, the health survey data that can represent the full crowd in Shanghai City and all residents in Shanghai City Data are treated, using advanced big data information analysis and calculating step is excavated, carries out data multidimensional analysis and excavation, establishing can be complete The health life expectancy in life expectancy index system of the serving span of population health situation is reflected in face, is the overall merit of crowd's health status, Effective configuration of health resources provides decision-making foundation.
One, cross-platform integration hygiene medical treatment big data
(1) hygiene medical treatment data collection
1. disease surveillance data: mainly including Birth Registration data, Vital registration data, tumour registration data, infectious disease Registration data etc.；
2. crowd's self-appraisal health data: in a manner of phased-mission system, carry out Shanghai Residents self-appraisal health survey, Obtain the self-appraisal health data that can represent the full crowd in Shanghai City；
3. resident's diagnosis and treatment data: obtaining medical person-time of the various diseases of Shanghai City different crowd, number of patients, the course of disease and control The related datas such as treatment expense；
4. group of handicapped's registration data: obtaining all kinds of disabled distribution situations of different crowd, severity and disabled allowance With etc. data；
(2) multi-source magnanimity isomeric data integration
1. encoding using International Classification of Diseases coding (ICD-10) to the disease in multi-source data, disease number is realized According to standardization；
2. missing data fills up technology, according to different data feature, using statistics pair such as mean value, median and modes Missing data is filled up；
3. the multi-source data of same disease are carried out heterogeneous analysis first, are used after rejecting unreasonable data Meta analysis method carries out aggregation of data；
Two, health life expectancy in life expectancy analysis knowledge library is established
(1) health life expectancy in life expectancy classification of diseases special topic knowledge data base is established
Health life expectancy in life expectancy measuring and calculating has a set of classification system of itself to various disease and its sick disability weight, it both with ICD is associated and has any different.The present invention constructs the disease federated database including 4 hierarchical relationships, will be compiled based on ICD-10 The Shanghai disease surveillance data of code and sick disability weight organically combine.
(2) the disability weight database of Health needs longevity measuring and calculating is established
This research adjusts weight using thousand kinds of diseases of Global disease burden and the disability of Health outcome.But true people In group, in the prevalence of and the case where suffer from a variety of diseases, this morbidity is based on multiple diseases concurrently to the independence of healthy influence It is assumed that the healthy weight concurrent using the multiple diseases of following calculation using models, and establish disability weight database.
Assuming that someone suffers from n kind disease, the healthy weight of every kind of disease is ωi(i=1 ... n) then merges n kind disease Healthy weight are as follows:
Three, health life expectancy in life expectancy measuring and calculating analysis model is established
(1) sick disability adjusts life expectancy
1. each age group crowd Weight Measurement: acquiring full crowd's by health life expectancy in life expectancy application support information system Disease, wound, residual information, aggregate into the disease, wound, residual prevalence rate of all kinds of each age group crowd, and corresponding various diseases/ Disability merges disease/disability weight information.
Prevalence rate of the various diseases in crowd
According to the prevalence rate of each age group crowd disease, wound, residual state, and corresponding disability weight, each age is calculated The disability weight of group.
I (i=1 ... k) is disease/disability type, equipped with k kind disease/disability or merges disease/disability type；j(j =1 ... 85+), indicate age group.
2.Sullivan method calculates health life expectancy in life expectancy: constructing Life Table according to demographic data and Accidental mortality data.With Based on Life Table, using Sullivan method, weight is adjusted according to the population health that measuring and calculating obtains in conjunction with disease, wound, residue, measuring and calculating is adjusted Survival person-year after whole, finally obtains health life expectancy in life expectancy.
(1) life expectancy is calculated:
The measuring and calculating of the age group death rate:Refer to the average mortality in x~x+n years old
Probability of death measuring and calculating:
Number of survivors measuring and calculating: l0×nqx=ndx, lx+n=lx-ndx
The measuring and calculating of Life Table population:1L0=l1+a0·d0,
Survive total person-time:
Life expectancy:
(2) calculate health life expectancy in life expectancy model
It enables,
Dx(x, x+5) year between disability weight prevalence rate
YDx=Lx*Dx(x, x+5) year between disability lose life years
YWDx=Lx*(1-Dx) healthy life span between (x, x+5) year
Then x years old when health life expectancy in life expectancy HALExAs YWDiSum, wherein i=x ... ω.
Corresponding health impact service life year LHExCalculating it is as follows:
3. health impact caused by sick disability measures
(1) loss (years in service life year caused by different reasons death is calculated on the basis of different crowd Life Table Of life lost, YLL), calculation formula is as follows:
YLL=NCe(ra)/(β+r)2[e-(β+r)(L+a)[-(β+r)(L+a)-1]-e-(β+r)a[-(β+r)a-1]]
L: the age group mean age at death calculated by standard life expectancy；
N: the death toll of the specific cause of disease；
R: discount rate；
C: age weight corrects constant；
β: age weight parameter；
A: age of onset.
(2) loss of disability caused by different reasons life years (years is calculated on the basis of different crowd Life Table Lived with disability, YLD),
Calculation formula is as follows:
YLD=IDWCe(ra)/(β+r)2[e-(β+r)(L+a)[-(β+r)(L+a)-1]-e-(β+r)a[-(β+r)a-1]]
I: the morbidity number of cases of each disease；
DW: the corresponding disability weight of various diseases；
L: the course of disease；
R: discount rate；
C: age weight corrects constant；
β: age weight parameter；
A: age of onset.
(2) self-appraisal health life expectancy in life expectancy
1. crowd's self-appraisal health survey
The method that the present invention uses multistage stratified random smapling, Shanghai City population ages' structure spy can be represented by having extracted 20640 people of sign carry out crowd's self-appraisal health survey.Application form includes self-report health and scene describes two parts, self-report health 8 fields of reflection crowd's self-assessment: mobility, self-care ability, pain discomfort, cognition, communicative competence, visual capacity, The health status of sleep quality, mood；Scene description section is equipped with 2 hypothesis problems, in 8 dimensions to reflect investigation Object is illustrative to self-report health problem, the correction for self-report health.
2. application Chopit model corrects self-report health
The present invention is in the orderly probabilistic model of combined grade (Chopit, the compound hierarchical that WHO is developed Ordered probit model) correction is displaced because of point of contact caused by the individual sociological characteristics such as age, gender, educational background, area Bias and top effect on the basis of obtaining the health data with each dimension being comparable between crowd, create Information Entropy, and Using Principal Component Estimation method, the weight of different healthy dimensions is calculated, for the comprehensive assessment to general health.It is specific to calculate Steps are as follows:
(1) self-report health part:
Wherein xiFor covariant, β is fixed effect, εiFor residual error item, εi~N (0,1)
The self-appraisal result of respondent i is yi, it is by latent variableA set of point of contact on continuous scaleIt determines
It is expressed as personal characteristics νiFunction, γk′To estimate parameter.
(2) healthy scene part:
θjFor the true health of the respondent i imaginary personage described to j-th of healthy scene (j=1,2 ..., J).For Respondent for illusion personage's health status sensation level,
It is same to assume zijIt is by latent variableA set of point of contact on continuous scaleIt determines.
(3) identification and correction at point of contact:
Self-rated Health questionnaire use 5 class response option of Likert, respectively indicate " l is without difficulty ", " 2 Mild difficulty ", " in 3 Degree difficulty ", " 4 severes are difficult ", " 5 is extremely difficult ".Primarily look at healthy scene point of contact l (τ1) " no difficulty " to " Mild difficulty " Transition influence factor, then see the symbol and statistical significance of the regression coefficient of the corresponding influence factor in other each point of contacts.If certain Covariant is with respect to τ2,τ3,τ4Regression coefficient symbol corresponding thereto in τ1Symbol it is consistent and statistically significant, then claim should Covariant causes the displacement of " entirety " property point of contact, and prompting the covariant is the key variables for influencing self-report health data comparativity. Under the assumed condition of " response consistency ", respondent reports that a set of point of contact standard of own health level reports scene with it Described in the general level of the health point of contact standard it is consistent, then can use the cusp position of information " anchoring " individual of situation assessment, The information for obtaining individual self-report health standard, using maximum-likelihood method obtained from the parameter Estimation of report health model, to realize certainly Report the correction of health.
(4) crowd's self-appraisal health weight is calculated:
To the hypothesis problem of each dimension Scene Simulation, by calculating comentropy (H (X)=- ∑x∈XP (x) logP (x), In, P (x) is the probability that each value may be fetched into every group of number) weight that determines each problem is somebody's turn to do by weighted calculation The comprehensive score of dimension finally uses principal component model, and the health assessment of different latitude is comprehensive at a total self-appraisal score. Healthy weight uses [0,1] interval scale, and 0 represents health completely, and 1 represents death.
y2Estimate to adjust to the disability behind [0,1] section, y1It is Chopit model to the people after the correction of crowd's self-report health Group's disability score, ymaxAnd yminRespectively represent minimum and maximum score.
(5) self-appraisal health life expectancy in life expectancy is calculated using Sullivan method
The characteristic of this research: the present invention distinguishes from objective (disease and disability) and subjective (self fitness assessment) 2 angles Establish health life expectancy in life expectancy measuring and calculating and analysis model, model built can be used for excavating influence Shanghai Residents health status and The principal disease and influence factor of serving span, the prevention and control of formulation and disease to health policy, and promote Shanghai Residents Understanding and attention to serving span have important guiding effect.
The present invention is upper in application, comprehensively utilizes health care big data, integrates existing various diseases monitoring information, in conjunction with Crowd cross section assessment surveys data establishes Shanghai Residents health life expectancy in life expectancy index measuring and calculating method and data acquisition system, Shanghai Residents health life expectancy in life expectancy index is illustrated to comprehensive, various dimensions, health life expectancy in life expectancy index missing status is changed.
What it is using the realization health life expectancy in life expectancy operational analysis function based on hygiene medical treatment big data in the invention is System and method provide unified, standardized automatic measurement & calculation and intellectual analysis for the measuring and calculating of Shanghai City health life expectancy in life expectancy.
(1) health life expectancy in life expectancy data resource management platform.The building of knowledge type data warehouse: by literature search, specially Family's consulting and web crawlers, convergence health life expectancy in life expectancy calculate relevant knowledge data, take into account that data query utilizes convenient and The expansion of information analysis dimension, the star-like data bins of knowledge of the information such as building collection classification of diseases system, healthy weight, risk factor Library, the sustainability for health life expectancy in life expectancy analysis lay the foundation；The building of analytic type data warehouse: long-acting smooth number is relied on It is the extraction of data resource and historical data needed for realizing health life expectancy in life expectancy, cleaning, fractionation, reconstruct, whole according to collecting channel It closes, and is arranged, integrated by information resources data framework, data standard specification etc., and integrated according to unified data model Landing, formation is sought unity of standard, unification indexes, so that medical treatment relevant to health life expectancy in life expectancy, hygiene information resources progress are effectively Grasp and management, for health care information resources in-depth using laying the foundation.Major function includes: source data management, letter Cease inventory management of resources, information resources Mapping and Converting, data intelligence integration, data exchange standard management etc..
(2) health life expectancy in life expectancy data application service platform: the health care that data-oriented resource management platform has been integrated Information resources are calculated according to health life expectancy in life expectancy and analyze demand to data progress taxonomic revision, and establish index directory index, To realize the shared of data resource and utilize.In addition to this, health life expectancy in life expectancy data application service platform combination Shanghai City is strong The existing Quality Control Mechanism of health Information Network is formed for big data using middle finger target, application process carries out quality of data prison It surveys, assessment, cleaning, improves index application confidence level.Major function includes: operating mechanism and standardized administration, data quality accessment And verification, operation maintenance management, main body management.
(3) health life expectancy in life expectancy big data specific analysis platform: all kinds of business collected based on data resource management platform The population health that data and sample investigation obtain assesses data, by statistical model and big data analysis technology, completes a system Column health life expectancy in life expectancy index system, realizing has effect to Shanghai City health care big data based on health life expectancy in life expectancy theme With.Major function includes: data management and maintenance, sample investigation management, the management of health life expectancy in life expectancy index system, Health needs (life expectancy, disease-free life expectancy are adjusted without disability life expectancy, self-appraisal health life expectancy in life expectancy, sick disability for service life specific analysis Life expectancy), comprehensive analysis etc..Specific module has: data correlation and maintenance: according to data quality accessment result to data exchange And incidence relation involved in resource impact carries out periodic maintenance；It is formed according to data using demand and existing data correlation is closed System carries out the maintenance that additions and deletions change etc.；Sample investigation management: it realizes to the Sampling Frame management of sample investigation, sampling of data, sample database The functions such as management, survey item management.Sampling Frame includes the list or register of all target samples, can be checked existing all The list of Sampling Frame can carry out data addition, deletion, modification, batch importing/export etc..It can be according to required by different investigation Sample range, sample size and the methods of sampling, obtain sample data.Realize the Classification Management of Different categories of samples, detail are safeguarded, The functions such as analyses and comparison.Unified storage and management are carried out to survey item, record the project name of all previous investigation, responsible department, Survey objective, etc. essential informations, realize data addition, deletions, modification, batch importing/export, filter inquire etc. functions, mainly It is to play filing and management role after the completion of sample investigation project.Health life expectancy in life expectancy specific analysis: in information resource platform On the basis of, it realizes that abridged life table and complete Life Table calculate step, the comprehensive analysis of life expectancy is completed, in conjunction with the healthy phase It hopes the knowledge data base of lifetime data resource management platform, realizes sick disability and risk factor measuring and calculating, construct sick disability adjustment period It hopes service life specific analysis modeling tool, completes sick disability and adjust life expectancy specific analysis.Based on population health sample investigation number According to Chopit model to the general health and pain of different regions and age groups crowd, motor function, mood The general level of the health of 8 dimensions such as anxiety is corrected respectively, realizes the health evaluating based on self-appraisal data, and application of results according to this Sullivan method calculates resident's self-appraisal health life expectancy in life expectancy.
(4) health life expectancy in life expectancy big data space-time shows platform.By arranging from time and Spatial Dimension, analysis shows respectively Area health life expectancy index of correlation, and pass through the analysis methods such as space-time cross correlation, fluoro data potential value information, hair It now hides rule and off-note, the principal disease and risk factor of analyzing influence Shanghai Residents Health needs mentions for decision For foundation.Major function includes: based on spatio-temporal multidimensional data analysis, health life expectancy in life expectancy report management etc..
In this description, the present invention is described with reference to its specific embodiment.But it is clear that can still make Various modifications and alterations are without departing from the spirit and scope of the invention.Therefore, the description and the appended drawings should be considered as illustrative And not restrictive.

Claims (30)

1. a kind of system of the realization health life expectancy in life expectancy operational analysis function based on hygiene medical treatment big data, which is characterized in that The system includes:
Hygiene medical treatment big data subsystem, for collecting and integrating hygiene medical treatment data；
Health life expectancy in life expectancy analytical database management subsystem, for being collected simultaneously according to the hygiene medical treatment big data subsystem Hygiene medical treatment data after integration establish the data base management subsystem of measuring and calculating health life expectancy in life expectancy, the health life expectancy in life expectancy Analytical database management subsystem is connected with the hygiene medical treatment big data subsystem；
Health life expectancy in life expectancy calculates analysis model subsystem, and for calculating health life expectancy in life expectancy, the health life expectancy in life expectancy is surveyed Point counting analysis model subsystem is connected with the health life expectancy in life expectancy analytical database management subsystem.
2. the realization health life expectancy in life expectancy operational analysis function according to claim 1 based on hygiene medical treatment big data is System, which is characterized in that the hygiene medical treatment big data subsystem includes:
Hygiene medical treatment data collection module, for collecting hygiene medical treatment data；
Multi-source magnanimity isomeric data integration module, for encoding to the hygiene medical treatment data after collection, described comes more Source magnanimity isomeric data integration module is connected with the hygiene medical treatment data collection module.
3. the realization health life expectancy in life expectancy operational analysis function according to claim 1 based on hygiene medical treatment big data is System, which is characterized in that the health life expectancy in life expectancy analytical database management subsystem includes:
Health life expectancy in life expectancy classification of diseases special topic knowledge data database management module, with the hygiene medical treatment big data subsystem phase Connection, for calculating the joint data of management various disease and its sick disability weight；
Health life expectancy in life expectancy calculates disability weighted data database management module, knows with the health life expectancy in life expectancy classification of diseases special topic Know database management module to be connected, manages the concurrent healthy weighted data of multiple diseases for calculating.
4. the realization health life expectancy in life expectancy operational analysis function according to claim 1 based on hygiene medical treatment big data is System, which is characterized in that the health life expectancy in life expectancy measuring and calculating analysis model subsystem includes sick disability adjustment expectation durability analysis Module and self-appraisal health life expectancy in life expectancy analysis module, the sick disability adjustment expectation durability analysis module and self-appraisal Health needs Durability analysis module is connected with the health life expectancy in life expectancy analytical database management subsystem.
5. the realization health life expectancy in life expectancy operational analysis function according to claim 4 based on hygiene medical treatment big data is System, which is characterized in that the described sick disability adjustment expectation durability analysis module include each age group crowd Weight Measurement submodule, Health impact caused by health life expectancy in life expectancy computational submodule and sick disability measures submodule, and each age group crowd weight is surveyed Health impact measurement submodule, which is sequentially connected, caused by operator module, health life expectancy in life expectancy computational submodule and sick disability connects.
6. the realization health life expectancy in life expectancy operational analysis function according to claim 5 based on hygiene medical treatment big data is System, which is characterized in that each age group crowd Weight Measurement submodule analyzes data by the health life expectancy in life expectancy Data in the management subsystem of library calculate the disability weight of each age group.
7. the realization health life expectancy in life expectancy operational analysis function according to claim 5 based on hygiene medical treatment big data is System, which is characterized in that the health life expectancy in life expectancy computational submodule includes life expectancy measuring and calculating unit and health life expectancy in life expectancy Calculation using models unit, the life expectancy measuring and calculating unit are connected with each age group crowd Weight Measurement submodule, The health life expectancy in life expectancy calculation using models unit is connected with the life expectancy measuring and calculating unit.
8. the realization health life expectancy in life expectancy operational analysis function according to claim 5 based on hygiene medical treatment big data is System, which is characterized in that the measurement submodule of health impact caused by the sick disability includes that the lethal life loss of different reasons is surveyed The life years loss measuring and calculating unit that calculation unit and different reasons are caused injury, the loss of the different reasons lethal service life year is calculated single The life years loss measuring and calculating unit that first and different reasons are caused injury is connected with the health life expectancy in life expectancy computational submodule.
9. the realization health life expectancy in life expectancy operational analysis function according to claim 4 based on hygiene medical treatment big data is System, which is characterized in that the self-appraisal health life expectancy in life expectancy analysis module includes self-appraisal health survey submodule and self-report health Correction module is calculated, the self-appraisal health survey submodule and the health life expectancy in life expectancy analytical database manage subsystem System is connected, and the self-report health calculates correction module and is connected with the self-appraisal health survey submodule.
10. the realization health life expectancy in life expectancy operational analysis function according to claim 9 based on hygiene medical treatment big data System, which is characterized in that the self-report health calculates correction module and includes self-report health data pre-calculation unit, reports and be good for certainly Health data correction unit and self-report health Data Computation Unit, the self-report health data pre-calculation unit, self-report health number It is sequentially connected according to correction unit and self-report health Data Computation Unit, the self-report health data pre-calculation unit and described Self-appraisal health survey submodule is connected.
11. the realization health life expectancy in life expectancy operational analysis function according to claim 9 based on hygiene medical treatment big data System, which is characterized in that the self-report health data pre-calculation unit includes self-report health part precomputation subelement and is good for Health scene part precomputation subelement, the estimated operator list of the self-report health part precomputation subelement and healthy scene part Member is connected with the self-appraisal health survey submodule.
12. a kind of be based on the realization health life expectancy in life expectancy operational analysis of hygiene medical treatment big data using system described in claim 1 Control method, which is characterized in that the method the following steps are included:
(1) hygiene medical treatment big data subsystem described in collect the hygiene medical treatment data of different crowd and to the data of multi-source into Row integration；
(2) the health life expectancy in life expectancy analytical database management subsystem described in collects the hygiene medical treatment big data subsystem Data after integration establish database；
(3) the health life expectancy in life expectancy measuring and calculating analysis model subsystem described in is according to the health life expectancy in life expectancy analytical database pipe It manages the data in subsystem and calculates health life expectancy in life expectancy, and show the health life expectancy in life expectancy；
13. according to claim 12 realize health life expectancy in life expectancy operational analysis controlling party based on hygiene medical treatment big data Method, which is characterized in that the step (1) the following steps are included:
(1.1) the hygiene medical treatment big data subsystem described in collects the hygiene medical treatment data of different crowd；
(1.2) the hygiene medical treatment big data subsystem described in integrates the isomeric data of multi-source.
14. according to claim 13 realize health life expectancy in life expectancy operational analysis controlling party based on hygiene medical treatment big data Method, which is characterized in that the hygiene medical treatment data that the hygiene medical treatment big data subsystem is collected include disease surveillance data, people Group's self-appraisal health data, resident's diagnosis and treatment data and group of handicapped's registration data.
15. according to claim 13 realize health life expectancy in life expectancy operational analysis controlling party based on hygiene medical treatment big data Method, which is characterized in that the step (1.2) the following steps are included:
Hygiene medical treatment big data subsystem described in (1.2.1) is encoded according to International Classification of Diseases in the isomeric data of multi-source Disease data encoded；
Hygiene medical treatment big data subsystem described in (1.2.2) fills up missing data according to different data feature；
Hygiene medical treatment big data subsystem described in (1.2.3) carries out heterogeneous analysis to the multi-source data of same disease and goes forward side by side Row aggregation of data.
16. according to claim 12 realize health life expectancy in life expectancy operational analysis controlling party based on hygiene medical treatment big data Method, which is characterized in that the step (2) the following steps are included:
(2.1) health life expectancy in life expectancy analytical database management subsystem building various disease and its sick disability weight joint described in The database of data；
(2.2) the health life expectancy in life expectancy analytical database management subsystem described in calculates the concurrent healthy weight of multiple diseases, and Construct disability weight database.
17. according to claim 12 realize health life expectancy in life expectancy operational analysis controlling party based on hygiene medical treatment big data Method, which is characterized in that the healthy weight of the multiple diseases of measuring and calculating concurrently in the step (2.2), specifically:
The concurrent healthy weight of multiple diseases is calculated according to the following formula:
Wherein, n is kinds of Diseases, ωi(i=1 ... n) is the healthy weight of every kind of disease.
18. according to claim 12 realize health life expectancy in life expectancy operational analysis controlling party based on hygiene medical treatment big data Method, which is characterized in that the health life expectancy in life expectancy measuring and calculating analysis model subsystem includes sick disability adjustment expectation durability analysis Module, the measuring and calculating disease disability in the step (3) adjust life expectancy, specifically includes the following steps:
Sick disability adjustment expectation durability analysis module described in (3-1.1) aggregates out each age group crowd disease, wound, residual state Prevalence rate and corresponding disability weight, and calculate the disability weight of each age group；
Sick disability adjustment expectation durability analysis module described in (3-1.2) calculates life expectancy and health life expectancy in life expectancy；
Sick disability described in (3-1.3) adjusts the health impact value caused by expectation durability analysis module is calculated because of sick disability.
19. according to claim 18 realize health life expectancy in life expectancy operational analysis controlling party based on hygiene medical treatment big data Method, which is characterized in that the disability weight of each age group of calculating in the step (3-1.1), specifically:
The disability weight of each age group is calculated according to the following formula:
Wherein, i (i=1 ... k) is disease/disability type, and k is disease, disability or the type sum for merging disease disability, j (j =1 ... 85+) it is age group.
20. according to claim 18 realize health life expectancy in life expectancy operational analysis controlling party based on hygiene medical treatment big data Method, which is characterized in that the step (3-1.2) the following steps are included:
Sick disability adjustment expectation durability analysis module described in (3-1.2.1) calculates life expectancy；
Sick disability adjustment expectation durability analysis module described in (3-1.2.2) calculates Health needs according to the life expectancy Service life and corresponding health impact service life year.
21. according to claim 20 realize health life expectancy in life expectancy operational analysis controlling party based on hygiene medical treatment big data Method, which is characterized in that the calculating life expectancy in the step (3-1.2.1), specifically:
Life expectancy is calculated according to the following formula:
Wherein, TxTotal person-time for survival, lxIt survives when being x years old number, exThe life expectancy of crowd when being x years old.
22. according to claim 20 realize health life expectancy in life expectancy operational analysis controlling party based on hygiene medical treatment big data Method, which is characterized in that the calculating health life expectancy in life expectancy in the step (3-1.2.2), specifically:
Health life expectancy in life expectancy is calculated according to the following formula:
Wherein, YWDxFor the healthy life span between x years old to x+5 years old, ω is the highest age group in Life Table, lxWhen being x years old Survival number, HALExThe health life expectancy in life expectancy of crowd at as x years old.
23. according to claim 20 realize health life expectancy in life expectancy operational analysis controlling party based on hygiene medical treatment big data Method, which is characterized in that the calculating health impact service life year in the step (3-1.2.2), specifically:
The health impact service life year is calculated according to the following formula:
Wherein, YDxFor the disability life span between x years old to x+5 years old, LExThe life expectancy of crowd, HALE when being x years oldxIt is strong Health life expectancy, LHExFor the health impact service life year.
24. according to claim 18 realize health life expectancy in life expectancy operational analysis controlling party based on hygiene medical treatment big data Method, which is characterized in that because the health impact value caused by sick disability includes the different reasons lethal longevity in the step (3-1.3) The life years costing bio disturbance processing that the processing of life year costing bio disturbance and different reasons are caused injury, the different reasons lethal service life year Costing bio disturbance processing, specifically:
Penalty values YLL of different reasons lethal service life year is calculated according to the following formula:
YLL=NCe(ra)/(β+r)2[e-(β+r)(L+α)[-(β+r)(L+α)-1]-e-(β+r)α[- (β+r) α -1]],
Wherein, L is the age group mean age at death calculated by standard life expectancy, and N is the death toll of the specific cause of disease, and r is Discount rate, C are that age weight corrects constant, and β is age weight parameter, and a is age of onset；
The life years costing bio disturbance processing that the different reasons are caused injury, specifically:
The life years penalty values YLD that different reasons are caused injury is calculated according to the following formula:
YLD=IDWCe(rα)/(β+r)2[e-(β+r)(L+α)[-(β+r)(L+α)-1]-e-(β+r)α[- (β+r) α -1]],
Wherein, I is the morbidity number of cases of each disease, and DW is the corresponding disability weight of various diseases, and L is the course of disease, and r is discount rate, and C is Age weight corrects constant, and β is age weight parameter, and a is age of onset.
25. according to claim 12 realize health life expectancy in life expectancy operational analysis controlling party based on hygiene medical treatment big data Method, which is characterized in that the health life expectancy in life expectancy measuring and calculating analysis model subsystem includes self-appraisal health life expectancy in life expectancy analysis mould Block, the measuring and calculating self-appraisal health life expectancy in life expectancy in the step (3), specifically includes the following steps:
Self-appraisal health life expectancy in life expectancy analysis module described in (3-2.1) collects the self-appraisal health survey of each crowd；
Self-appraisal health life expectancy in life expectancy analysis module described in (3-2.2) calculates self-report health data, and according to crowd's self-appraisal health Weight is corrected calculating to data.
26. according to claim 27 realize health life expectancy in life expectancy operational analysis controlling party based on hygiene medical treatment big data Method, which is characterized in that the self-appraisal health survey of each crowd includes that self-report health part and scene are retouched in the step (3-2.1) State part.
27. according to claim 27 realize health life expectancy in life expectancy operational analysis controlling party based on hygiene medical treatment big data Method, which is characterized in that the step (3-2.2), comprising the following steps:
Self-appraisal health life expectancy in life expectancy analysis module described in (3-2.2.1) calculates the health data of self-report health part and scene is retouched State the health data of part；
Self-appraisal health life expectancy in life expectancy analysis module described in (3-2.2.2) identifies and corrects point of contact；
Self-appraisal health life expectancy in life expectancy analysis module described in (3-2.2.3) calculates crowd's self-appraisal health weight；
Self-appraisal health life expectancy in life expectancy analysis module described in (3-2.2.4) calculates the self-appraisal Health needs longevity according to Sullivan method Life.
28. according to claim 29 realize health life expectancy in life expectancy operational analysis controlling party based on hygiene medical treatment big data Method, which is characterized in that the health data of self-report health part is calculated in the step (3-2.2.1), specifically:
The health data y of self-report health part is calculated according to the following formulai *Y:
yi *=xiβ+εi；
Wherein, xiFor covariant, β is fixed effect, εiFor residual error item.
29. according to claim 29 realize health life expectancy in life expectancy operational analysis controlling party based on hygiene medical treatment big data Method, which is characterized in that the health data of the computation scenarios description section in the step (3-2.2.1), specifically:
The health data z of scene description section is calculated according to the following formulaij:
Wherein, θjIt is respondent i to the true health of the imaginary personage of j-th of healthy scene description.
30. according to claim 29 realize health life expectancy in life expectancy operational analysis controlling party based on hygiene medical treatment big data Method, which is characterized in that calculating crowd's self-appraisal health weight in the step (3-2.2.3), specifically:
Crowd's self-appraisal health weight is calculated according to the following formula:
Wherein, y2Estimate to adjust to the disability behind [0,1] section, y1After being corrected for Chopit model to crowd's self-report health Crowd's disability score, ymaxAnd yminRespectively represent minimum and maximum score.
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