WO2021145740A1 - Group health index evaluation method and computer program for executing same - Google Patents

Abstract

One embodiment of the present invention provides a group health index evaluation method for evaluating a health index of a group on the basis of the health status of group members, the method comprising the steps of: for a plurality of preset major disease factors, receiving prevalence information of the group members for each of the preset major disease factors from a database containing factor-control study information; receiving tardiness-and-absence information of the group members for each of the major disease factors; and using the prevalence information for each of the major disease factors and the tardiness-and-absence information of the group members to derive a productivity loss prediction model for predicting productivity loss in the group.

Description

Organizational health index evaluation method and computer program executing the same

Embodiments of the present invention relate to a method for evaluating a tissue health index and a computer program for executing the same.

In recent years, the importance of health management of organizations, including companies, is increasing from the viewpoint of promoting the health of members within the organization to improve the productivity of the organization. Health management refers to a management method that promotes organizational performance improvement by creating a work environment that can motivate healthy work performance.

In order to effectively implement such health management, objective and detailed indicators are needed that sufficiently reflect the management environment of the organization and the characteristics of its members. In other words, it is necessary to objectively grasp the health level according to the living and working environment of various members, physiological factors, etc., and to reflect the characteristics of member productivity from the point of view of organizational management rather than individual health of each member. However, since the conventional tissue health index is based only on questionnaire data rather than a biomarker that can objectively represent a member's health status, biased interpretation by self-reported is concerned.

The above-mentioned background art is technical information possessed by the inventor for derivation of the present invention or acquired in the process of derivation of the present invention, and cannot necessarily be said to be a known technique disclosed to the general public prior to the filing of the present invention.

Embodiments of the present invention by deriving a productivity loss prediction model using a database including factor control research information and questionnaire data of members, a method for evaluating the organizational health index for predicting the productivity loss of the organization and a computer program executing the same would like to provide

An embodiment of the present invention is a tissue health index evaluation method for evaluating the health index of an organization according to the health status of a member, wherein the member's said member with respect to a plurality of major disease factors preset from a database including factor control study information Receiving the prevalence information for each major disease factor, receiving the time and attendance information of the member according to each of the major disease factors, and the loss of productivity of the tissue using the prevalence information for each major disease factor and the member's time and attendance information It provides a method for evaluating the tissue health index, including the step of deriving a productivity loss predictive model for predicting.

In one embodiment of the present invention, the attendance information of the member according to each of the major disease factors may be provided by using the questionnaire data of the member.

In one embodiment of the present invention, the step of deriving the productivity loss predictive model comprises the steps of calculating the productivity loss effect for each major disease factor from the questionnaire data of the member, and the prevalence information for each major disease factor and the It may include deriving a productivity loss predictive model for predicting the productivity loss by using the productivity loss effect for each major disease factor.

In one embodiment of the present invention, the questionnaire data is data on Absenteeism and Presenteeism of a member due to the major disease factor, and the Absenteeism is the member due to the major disease factor. of absenteeism or early departure, and the presentism may be the degree of loss of productivity of members due to the major disease factor.

In one embodiment of the present invention, the productivity loss effect by each major disease factor and the productivity loss degree may be calculated by the following Equations (1) and (2), respectively.

Effect of productivity loss due to major disease factor i

Loss of productivity of organization m

provided that LPAi is (absentism time for a first preset period due to major disease factor i)/(total working hours during said first period), and LPPi is said first time period due to major disease factor i The degree of presentism during the period,

and pim is the prevalence of major disease factor i in tissue m.

In one embodiment of the present invention, the step of deriving the productivity loss prediction model comprises setting the degree of influence on productivity loss for each major disease factor as an absolute influence on the degree of productivity loss of the major disease factor, and The method may further include setting the product of the ratio of the effect of the productivity loss for each disease factor and the ratio of the number of members having the major disease factor to the total number of members of the tissue as the relative influence of the major disease factor on the productivity loss.

In one embodiment of the present invention, the step of deriving the productivity loss predictive model sets the absolute productivity loss degree of the organization by standardizing the productivity loss degree on the basis of a case in which no productivity loss occurs, The method may further include setting the relative productivity loss of the organization by standardizing the productivity loss on the basis of the maximum and minimum values of the productivity loss of the entire organization in the database including the factor control study information.

In an embodiment of the present invention, the step of deriving the productivity loss prediction model comprises calculating the total productivity loss, the total production time loss, and the total production wage loss by the following Equations (3) to (5), respectively. It may include further steps.

total loss of production of tissue m

total production time loss of tissue m

Total production wage loss of organization m

where Pm is the number of members of organization m, Tm is the average working hours of organization m, and Wm is the average wage of organization m.

In one embodiment of the present invention, the major disease factors may include cardiovascular disease, allergic disease, respiratory disease, digestive disease, depressive disease, and sleep disease.

In one embodiment of the present invention, the step of receiving the onset risk factor information of the member regarding a plurality of preset risk factors affecting the major disease factors from a database including the factor control study information Further comprising, the step of deriving the productivity loss predictive model may be a step of deriving the productivity loss predictive model further using the disease risk factor information.

In one embodiment of the present invention, the step of deriving the productivity loss predictive model includes deriving all pathways in which the onset risk factor affects the productivity loss, and the onset risk factor is determined according to the number of the pathways. By calculating the weight of the influence on the productivity loss, it is possible to derive a predictive model of the productivity loss.

In an embodiment of the present invention, the step of deriving the productivity loss predictive model includes calculating the sum total of changes in the net incidence of the major disease factors due to the onset risk factors, the prevalence by each major disease factor and the Calculating the effect of the onset risk factor on the prevalence of the major disease factor from the sum of changes in net incidence, and the effect of the onset risk factor on productivity loss from the effect of the onset risk factor on the prevalence of the major disease factor It may include calculating the weight of the disease risk factor and calculating the disease risk index from the weight of the effect on the productivity loss.

In one embodiment of the present invention, the sum of changes in the net incidence of the major disease factors due to the onset risk factors, the effect of the onset risk factors on the prevalence of the major disease factors, and the onset risk factors affect the productivity loss The weight of the influence and the disease risk index can be calculated by the following formulas (6) to (9).

Sum of changes in net incidence of major disease factor i due to risk factor j

Effect of risk factor j on prevalence of major disease factor i

Weight of the effect of risk factor j on productivity loss werTLPj

risk index

with the proviso that i is a major disease factor, j is an onset risk factor, n _ji is the net incidence of the major disease factor i by the onset risk factor j, p _j is the prevalence of the onset risk factor j,

is the incidence of major disease factor i among members without major disease factor i, without risk factor j,

is the odds ratio of the incidence rate for the major disease factor i of the risk factor j,

is the recovery rate of major disease factor i of members without risk factor j among members with major disease factor i,

is the odds ratio of the recovery rate for the major disease factor i of the onset risk factor j, and pi is the prevalence of the major disease factor i. In addition, the odds ratio is calculated from a database including the factor control study information.

In one embodiment of the present invention, the step of deriving the productivity loss predictive model comprises setting the absolute risk index of the tissue by standardizing the onset risk index on the basis of a case in which no productivity loss occurs, The method may further include setting the relative incidence risk index of the tissue by standardizing the onset risk index based on the maximum and minimum values of the onset risk index of the entire organization in the database including the factor control study information.

In one embodiment of the present invention, the onset risk factor includes a prior disease factor affecting the main disease factor, and a health-influencing factor affecting the main disease factor or the antecedent disease factor, and the onset risk The index may include a prior disease risk index due to the preceding disease factor and a health impact risk index due to the health influence factor.

In one embodiment of the present invention, the major disease factors include cardiovascular diseases, allergic diseases, respiratory diseases, digestive diseases, depressive diseases and sleep diseases, and the preceding disease factors are diabetes, hypertension, obesity, and high cholesterol. Including, the health-influencing factors may include smoking, lack of exercise, general stress, job stress, and drinking.

An embodiment of the present invention provides a computer program stored in a medium to execute any one of the methods described above using a computer.

Other aspects, features and advantages other than those described above will become apparent from the following drawings, claims, and detailed description of the invention.

The tissue health index evaluation method and the computer program executing the same according to embodiments of the present invention, the member's prevalence information for each major disease factor with respect to a plurality of major disease factors set in advance from a database including factor control study information And, by receiving the time and attendance information of the member according to each of the major disease factors, and using the prevalence information for each major disease factor and the time and attendance information of the member, a productivity loss prediction model for predicting the productivity loss of the tissue is derived. can do.

The organizational health index evaluation method and the computer program executing the same according to embodiments of the present invention use both the data that can objectively determine the health status of the member and the data that can identify the member's working environment as a predictive model for productivity loss By deriving this, it is possible to calculate the organizational health index that can organically understand the relationship between the health status of members and the loss of productivity of the organization, and through this, it is possible to identify the current level of productivity loss of the organization and predict the risk of potential loss of productivity there is.

1 is a flowchart schematically illustrating a method for evaluating a tissue health index according to an embodiment of the present invention.

2 is a graph relating to the health status of the member and the loss of productivity of the organization.

3 is a diagram illustrating an example of the factor control study information of FIG. 1 , wherein FIG. 3 (a) shows information on population composition by year, and FIG. 3 (b) shows information about a disease to be investigated by year.

4 is a flowchart for explaining in more detail the step of deriving the productivity loss prediction model of FIG. 1 .

Figure 5 is a view showing an example of applying the tissue health index evaluation method according to an embodiment of the present invention, Figure 5 (a) is an example of calculating the productivity loss of the organization, Figure 5 (b) is the main This is an example of calculating the absolute influence and the relative influence on productivity loss by disease factor.

6 is a flowchart schematically illustrating a method for evaluating a tissue health index according to another embodiment of the present invention.

7 is a flowchart illustrating the productivity loss prediction model of FIG. 6 .

8 is a diagram illustrating a pathway in which disease risk factors affect productivity loss.

9 is a flowchart for explaining in more detail the step of deriving the productivity loss prediction model of FIG.

10 is a diagram illustrating an example of an odds ratio used in the step of deriving the productivity loss prediction model of FIG. 6 .

11 is a graph comparing the degree of productivity loss and the incidence risk index between tissues, and FIG. 11 ( a ) shows the absolute productivity loss degree and the absolute incidence risk index, and FIG. 11 ( b ) shows the relative productivity loss degree and the relative incidence risk index. .

An embodiment of the present invention is a tissue health index evaluation method for evaluating the health index of an organization according to the health status of a member, wherein the member's said member with respect to a plurality of major disease factors preset from a database including factor control study information Receiving the prevalence information for each major disease factor, receiving the time and attendance information of the member according to each of the major disease factors, and the loss of productivity of the tissue using the prevalence information for each major disease factor and the member's time and attendance information It provides a method for evaluating the tissue health index, including the step of deriving a productivity loss predictive model for predicting.

Since the present invention can apply various transformations and can have various embodiments, specific embodiments are illustrated in the drawings and described in detail in the detailed description. Effects and features of the present invention, and a method of achieving them, will become apparent with reference to the embodiments described below in detail in conjunction with the drawings. However, the present invention is not limited to the embodiments disclosed below and may be implemented in various forms.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, and when described with reference to the drawings, the same or corresponding components are given the same reference numerals, and the overlapping description thereof will be omitted. .

In the following embodiments, terms such as first, second, etc. are used for the purpose of distinguishing one component from another, not in a limiting sense.

In the following examples, the singular expression includes the plural expression unless the context clearly dictates otherwise.

In the following embodiments, terms such as include or have means that the features or components described in the specification are present, and the possibility of adding one or more other features or components is not excluded in advance.

In the following embodiments, when it is said that a part such as a film, region, or component is on or on another part, not only the case where it is directly on the other part, but also another film, region, or component is interposed therebetween Including cases where there is

In the drawings, the size of the components may be exaggerated or reduced for convenience of description. For example, since the size and thickness of each component shown in the drawings are arbitrarily indicated for convenience of description, the present invention is not necessarily limited to the illustrated bar.

Where certain embodiments are otherwise feasible, a specific process sequence may be performed different from the described sequence. For example, two processes described in succession may be performed substantially simultaneously, or may be performed in an order opposite to the order described.

In the following embodiments, when a film, region, or component is connected, other films, regions, and components are interposed between the films, regions, and components as well as when the films, regions, and components are directly connected. It also includes cases where it is indirectly connected. For example, in this specification, when it is said that a film, a region, a component, etc. are electrically connected, not only the case where the film, a region, a component, etc. are directly electrically connected, but also other films, regions, components, etc. Indirect electrical connection is also included.

Hereinafter, a method for evaluating a tissue health index according to an embodiment of the present invention will be described with reference to FIGS. 1 to 5 .

1 is a flowchart for schematically explaining a method for evaluating a tissue health index according to an embodiment of the present invention, FIG. 2 is a graph related to the health status of a member and a loss of organizational productivity, and FIG. 3 is a factor contrast of FIG. As a diagram illustrating an example of research information, FIG. 3(a) shows information on population composition by year, and FIG. 3(b) shows information on a disease to be investigated by year. 4 is a flowchart for explaining in more detail the step of deriving the productivity loss prediction model of FIG. 1, and FIG. 5 is a view showing an example of applying the tissue health index evaluation method according to an embodiment of the present invention, FIG. 5(a) is an example of calculating the productivity loss of the tissue, and FIG. 5(b) is an example of calculating the absolute and relative influences on the productivity loss for each major disease factor.

According to FIG. 1 , the tissue health index evaluation method according to an embodiment of the present invention receives information on the prevalence of each major disease factor of a member regarding a plurality of major disease factors set in advance from a database including factor control research information. A productivity loss prediction model that predicts the degree of productivity loss of the organization using the step (S100), the step (S200) of receiving the member's attendance information according to each major disease factor, and the prevalence information for each major disease factor and the member's time and attendance information It may include the step of deriving (S300). The tissue health index evaluation method according to an embodiment of the present invention derives a productivity loss prediction model using a database including factor control research information and member's attendance information according to each major disease factor, thereby determining the health status of members. It is characterized in that the current productivity loss of the organization is identified. This will be described in more detail as follows.

Among the various factors that affect organizational productivity, the effect of the health status of members on organizational productivity is very important. Since members are individuals who directly compose an organization and perform organizational tasks, the health status of members can have a direct and long-term effect on organizational productivity. In addition, the national productivity loss due to the health status of the members is about 5% of GDP (US Chamber of Commerce (2016), Health and the Economy), so the health status of the members has a profound effect on the productivity loss of the company as well as the country. can affect

Factors that directly affect the working environment of employees can be largely divided into working conditions and employee environment. And factors affecting working conditions may include factors such as industry type, required skills and occupational type, employment type, working hours, shift work, and job autonomy. In addition, factors affecting the environment of members may include health management of members, welfare for members, and physical environment. These working conditions and the member environment affect the member's working environment, and the final member's health status can be derived by reflecting the individual member's resilience and other external stimuli. And the derived member's health status is expressed as member's attendance information that directly affects the productivity of the organization, that is, the member's Absenteeism and Presenteeism.

As shown in FIG. 2 , when the health status of the member is quantified, when the health status of the member is Q _{1 or} more, which is a predetermined value, it may be determined that there is no productivity loss due to the health status. On the other hand, when the health status of the member is _{greater than or equal to Q 2 , which} is a predetermined value, and less than Q ₁ , it may be determined that productivity loss due to the member's presentism occurs. Presentism means that the member went to work, but his performance decreased due to his or her health condition. If the member's health status worsens and Q is _{less than 1} , it may be determined that productivity loss due to the member's appcentism occurs. Absentism means that members are absent from work or leave early due to health conditions. That is, when the health status of the member is _{less than Q 1} , it may be determined that the productivity loss of the organization occurs due to the member's health status (presentism or absoluteism).

Taking this into consideration, countries and global companies around the world are implementing health management to improve the health of employees and prevent deterioration of health, thereby preventing organizational productivity loss.

In order for such health management to be effective, it is necessary to have an objective index that can organically derive the relationship between the health status of members and the loss of productivity of the organization while sufficiently reflecting the characteristics of the working environment and the health status of members within the organization. . However, until now, there is no objective indicator on the relationship between the health status of members and the productivity of the organization, and organizations including companies are experiencing difficulties in health management due to the lack of systematic data management on the health status of members.

In the conventional method, the self-report bias problem may occur because the organization itself responds to the work characteristics, the health status of the members, and the status of the members' health promotion activities. In addition, since the conventional method evaluates the management method of the organization rather than the direct health state of the member, it is difficult to derive a direct relationship between the health status of the member and the loss of productivity of the organization.

In addition, as another conventional method, in the case of a method of classifying and evaluating the health management structure and execution of an organization into five fields, the index is calculated by uniformly evaluating the weights for each field without varying the weight. In addition, since the conventional method calculates an index based on a questionnaire response, data that can objectively determine the health status of members, such as biomarkers, is lacking.

In order to solve this problem, the tissue health index evaluation method according to an embodiment of the present invention is the prevalence information for each major disease factor of the member with respect to a plurality of major disease factors set in advance from a database including factor control research information. And, by receiving the time and attendance information of the member according to each of the major disease factors, and using the prevalence information for each major disease factor and the time and attendance information of the member, a productivity loss prediction model for predicting the productivity loss of the tissue is derived. can do. Through this, the organizational health index evaluation method derives a productivity loss prediction model using both data that can objectively understand the health status of members and data that can understand the working environment of members, and the health status of members and organizational productivity It is possible to calculate a tissue health index that can organically determine the relationship of loss.

Here, the major disease factors refer to major diseases that directly affect productivity loss, and the risk factors to be described later refer to factors that do not directly affect productivity loss, but affect major disease factors. In addition, as will be described later, the risk factors for the onset may include antecedent disease factors and health-influencing factors. Antecedent disease factors are factors that increase the likelihood of developing a major disease agent in the future rather than causing a direct loss of productivity, and a health-influencing factor is a factor that increases the likelihood of developing a leading disease agent and/or major disease factors.

Hereinafter, the tissue health index evaluation method according to an embodiment of the present invention will be described in more detail.

First, prevalence information for each major disease factor of the member regarding a plurality of preset major disease factors is provided from a database including factor control research information (S100).

A factor-controlled study is a research method that investigates the relationship between a factor and a disease by tracing a group exposed to and without exposure to a specific factor and comparing the incidence rate of the disease being studied. It is also called a cohort study. In an embodiment of the present invention, factor control study information may be used to derive a predictive model for productivity loss as well as the prevalence for each major disease factor. In addition, the factor control study information can be utilized in predicting the degree of loss of tissue productivity due to the risk factors to be described later.

The database including the factor control study information according to an embodiment of the present invention, as shown in Figs. 3 (a) and 3 (b), the population composition (gender and gender) of subjects who have been screened for a preset period and age) and information on the prevalence, incidence, and recovery rate according to major disease factors by year. For example, the database may include information on subjects who have been screened from 2011 to 2016. Information on the prevalence, etc. of the disease to be investigated can be confirmed using biomarkers, etc. at the time of examination. However, this is only an example, and may additionally include more various information about the subject (member) to be examined and the disease.

Specifically, although the number and average age of the population divided according to gender for each year are described in FIG. 3A , the population composition may be categorized based on a specific age and gender. For example, the population may be divided into four groups by distinguishing men and women based on the age of 40. In addition, by collecting information on the prevalence, incidence, and recovery rate of the disease to be investigated for each of the divided population groups, it is possible to check the trend of the disease to be investigated according to the population composition. In addition, although only obesity and gastric diseases are the target diseases in FIG. 3( b ), more various diseases may be investigated as the target diseases. For example, in addition to obesity and stomach diseases, cardiovascular diseases, depressive diseases, sleep diseases, etc. may be included as diseases to be investigated. In addition, examination subjects in the database may be categorized for each organization. For example, by dividing the subject to be examined into members belonging to the organization A, organization B, and organization C, information on the disease to be investigated may be grasped for each organization.

The major disease factors refer to diseases that directly affect the loss of tissue productivity. In an embodiment of the present invention, major disease factors may include cardiovascular disease, allergic disease, respiratory disease, digestive disease, depression/anxiety disease, and sleep disease. Specifically, cardiovascular diseases include stroke, transient cerebral ischemia, angina pectoris, myocardial infarction, atrial fibrillation, etc., allergic diseases include rhinitis, atopic dermatitis, and allergic conjunctivitis, and respiratory diseases include chronic obstructive pulmonary disease (COPD), asthma, tuberculosis, etc. , digestive diseases may include reflux esophagitis, acute gastritis, chronic gastritis, etc., depressive diseases may include depressive disorders, anxiety disorders, etc., and sleep diseases may include sleep disorders.

The prevalence information for each major disease factor may be provided from a database including factor control study information as shown in FIG. 3 . In addition, as described above, the prevalence information for each major disease of the members of the organization may be provided from the prevalence information for each major disease factor categorized for each tissue.

Next, the member's attendance information according to each major disease factor is provided (S200). Time and attendance information of a member according to each major disease factor may be provided using questionnaire data about the member. More specifically, the questionnaire data may be data on the member's Absentism and Presentism due to a major disease factor. The specific questionnaire content is not limited, and it is sufficient if members within the organization can respond by dividing Abcentism and Presentism by major disease factors. For example, the questionnaire may be a questionnaire about the number of early departures or absences due to cardiovascular disease among major disease factors, the time away from work during working hours, or the degree of inability to concentrate on work due to an allergic disease. there is.

As described above, presentism may mean a degree to which a member cannot concentrate while working due to a health condition, and questionnaire data regarding presentism may be dependent on the individual member's judgment. Therefore, it is possible to set up a response guide reflecting specific and detailed working conditions such as type of industry, required skills and job type, employment type, working hours, and shift work. In this way, subjective and biased responses of members can be prevented.

Next, a productivity loss prediction model for predicting the productivity loss of the organization is derived using the prevalence information for each major disease factor and the attendance information of the members (S300).

The effect on productivity loss by major disease factors means the degree to which major disease factors individually affect the productivity loss of the tissue, and is calculated from the member's questionnaire data. For example, the units of Absentism and Presentism caused by sleep disorders among major disease factors may be appropriately manipulated and expressed as percentages, and this may be expressed as the effect of productivity loss for each major disease factor.

The productivity loss prediction model is derived based on the prevalence information for each major disease factor and employee attendance information. That is, the productivity loss prediction model according to an embodiment of the present invention predicts the productivity loss by using both data on major disease factors using biomarkers and the attendance information of members to predict the productivity loss of the organization. model can be built.

As shown in FIG. 4 , the step of deriving a productivity loss prediction model (S300) is a step of calculating the productivity loss effect by major disease factor from the member's questionnaire data (S310), the prevalence information for each major disease factor and the major The step of calculating the productivity loss for each major disease factor by using the productivity loss effect for each disease factor (S320), and calculating the total productivity loss by summing the productivity loss for each major disease factor (S330). can

First, the productivity loss effect for each major disease factor is calculated from the member's questionnaire data (S310). The effect on productivity loss for each major disease factor can be calculated by Equation 1 below.

[Equation 1]

Effect of productivity loss due to major disease factor i LPi=LPAi+LPPi

where LPAi is (absentism time during the first preset period due to major disease factor i)/(total working hours during the first period), and LPPi is the first time period due to major disease factor i It's about presentationism. also

am.

That is, the sum of Absentism and Presentism due to major disease factors may be set as the effect of productivity loss due to major disease factors. The first period is a value set in advance by reflecting the working conditions of the organization, such as the type of industry, required skills and occupational type, employment type, and whether to work in a shift. In an embodiment of the present invention, the first period may be one month. Also, the total working hours during the first period may be 160 hours.

In another embodiment, the industry or work type including irregular work may be determined based on the number of work hours rather than work hours within a specific period. For example, it is possible to set the number of shifts considering the average working hours per shift, and calculate the effect on productivity loss due to major disease factors by considering the Absentism or Presentism for the working hours corresponding to the number of shifts. there is.

Next, the productivity loss for each major disease factor is calculated using the prevalence information for each major disease factor and the productivity loss effect for each major disease factor (S320). The effect on productivity loss for each major disease factor can be calculated by Equation 2 below.

[Equation 2]

Loss of productivity for major disease factor i = LPi*pi

where _pi is the prevalence of major disease factor i.

That is, the productivity loss of the major disease factors can be calculated by multiplying the productivity loss effect of the major disease factors calculated from the questionnaire data of the members and the prevalence of the major disease factors set in advance from the database including the factor control study information.

Next, the total productivity loss of the tissue is calculated by summing the productivity loss for each major disease factor ( S330 ). The total productivity loss may be calculated by Equation 3 below.

[Equation 3]

Loss of productivity of organization m

where pim is the prevalence of major disease factor i in tissue m.

That is, the degree of loss of tissue productivity may be a value obtained by adding up degrees of loss of productivity according to a plurality of preset major disease factors.

An example of calculating the total productivity loss of an organization based on Equations 1 to 3 is shown in FIG. 5( a ). As shown in Fig. 5(a), cardiovascular disease, allergic disease, respiratory disease, digestive disease (esophagitis disease and stomach disease), depressive disease and sleep disease are set as six major disease factors. Next, based on the member's questionnaire data, the effect of productivity loss for each major disease factor expressed in Absentism and Presentism is calculated. Then, the productivity loss for each major disease factor is calculated by multiplying the calculated productivity loss effect for each major disease factor by the prevalence rate for each major disease factor. In addition, the total productivity loss of the tissue due to the major disease factors is calculated by summing the productivity loss for each major disease factor (calculated to two decimal places in FIG. 5(a)).

The total productivity loss of the tissue finally calculated as described above is a value calculated based on the prevalence information for each major disease factor and the attendance information of the members. That is, the calculated total productivity loss of the organization is a value calculated by reflecting both the numerical data and the working environment of the organization to which the members belong. Therefore, according to the organizational health index evaluation method according to an embodiment of the present invention, it is possible to quantitatively evaluate the productivity loss of the organization based on data that faithfully reflects the characteristics and working environment of the organization while being objective.

In addition, the step of deriving a productivity loss prediction model according to an embodiment of the present invention (S300) is to calculate the absolute influence and the relative influence of the major disease factors on the productivity loss from the productivity loss effects for each major disease factor. It may include further steps.

Specifically, in the effect of productivity loss for each major disease factor provided from the member's questionnaire data, the absolute influence of the major disease factor on the productivity loss may be set as the absolute or presentism for each major disease factor. In addition, the relative influence of the major disease factors on the productivity loss may be calculated as the product of the effect of the productivity loss for each major disease factor and the ratio of the number of members having the major disease factor to the total number of members of the tissue.

The calculated absolute influence on productivity loss for each major disease factor is the average value of the productivity loss effect for each major disease factor calculated from the questionnaire data of each member. Therefore, the absolute influence can be determined to the extent that one member affects the productivity loss. On the other hand, the calculated relative influence on productivity loss for each major disease factor is a value calculated by considering the number of members having the corresponding major disease factor and the total number of members in the organization. Therefore, the relative influence can be determined to the extent that it affects the productivity loss based on the organization.

An example of calculating the absolute and relative influences of major disease factors on productivity loss is shown in FIG. 5( b ). As shown in Fig. 5(b), in the case of absolute influence, it can be seen that depressive disease shows the greatest absolute influence in both Abcentism and Presentism. In addition, in the case of relative influence, it can be seen that sleep disorders show the largest relative influence in terms of Absentism, and digestive diseases show the largest relative influence in terms of Presentism. In this way, by calculating the absolute and relative influences of the major disease factors on the productivity loss, the effect of the major disease factors on the productivity loss of the member and the effect on the productivity loss of the organization can be quantitatively grasped.

On the other hand, in FIG. 5(b), absolute and relative influences on productivity loss by major disease factors were calculated by dividing Abcentism and Presentism. may be For example, the absolute and relative influences on productivity loss by major disease factors are calculated based on the LPi of productivity loss effects by major disease factors calculated by Equation 1, which is the sum of Abcentism and Presentism. You may.

In addition, the step of deriving a productivity loss prediction model according to an embodiment of the present invention (S300) may further include calculating the total productivity loss, the total production time loss, and the total production wage loss. Total lost productivity, total lost production time, and total lost wages are values that reflect the total number of members in the organization, respectively, the total loss of productivity in the organization, the total loss of working hours in the organization, and the total loss of wages in the organization due to the health status of the members, respectively. means The total productivity loss, the total production time loss, and the total production wage loss may be calculated by the following Equations 4 to 6, respectively.

[Equation 4]

total loss of production of tissue m

where Pm is the total number of members of the organization m.

That is, the total production loss may be a value regarding the productivity loss reflecting the total number of members in the organization.

[Equation 5]

total production time loss of tissue m

where Tm is the average working hours of organization m.

That is, the total production time loss may be a value related to the productivity loss reflecting the average working hours of the organization.

[Equation 6]

Total production wage loss of organization m

where Wm is the average wage of organization m.

That is, the total production wage loss may be a value related to the productivity loss reflecting the average wage of the organization.

Based on the productivity loss calculated in this way, various values representing the current state of the organization can be calculated, and can be used as an index for organizational management.

Hereinafter, a method for evaluating an organization's health index according to another embodiment of the present invention will be described with reference to FIGS. 6 to 10 .

6 is a flowchart for schematically explaining a tissue health index evaluation method according to another embodiment of the present invention, FIG. 7 is a flowchart showing the productivity loss prediction model of FIG. 6, and FIG. It is a diagram showing the path of influence. 9 is a flowchart for explaining in more detail the step of deriving the productivity loss prediction model of FIG. 6, and FIG. 10 is a view showing an example of an odds ratio used in the step of deriving the productivity loss prediction model of FIG. .

The tissue health index evaluation method according to another embodiment of the present invention comprises the steps of receiving, from a database including factor control research information, the prevalence information for each major disease factor of a member regarding a plurality of pre-set major disease factors (S100') , a step of receiving the member's risk factor information regarding a plurality of pre-set risk factors affecting the major disease factors from a database including factor control research information (S200'), the member's according to each major disease factor The step of receiving time and attendance information (S300') and the step of deriving a productivity loss prediction model that predicts the productivity loss of the organization using the prevalence information for each major disease factor and the member's attendance information and onset risk factor information (S400') ) is included. The tissue health index evaluation method according to another embodiment of the present invention uses a database including factor control research information and member's attendance information according to each major disease factor by deriving a productivity loss prediction model by using the member's health status. It is possible to predict the potential risk of loss of productivity in the organization

First, prevalence information for each major disease factor of a member regarding a plurality of preset major disease factors is provided from a database including factor control research information (S100'). This is the same as the step (S100) of receiving the prevalence information for each major disease factor of the member according to an embodiment of the present invention, and thus a description thereof will be omitted.

Next, the member's risk factor information is provided with respect to a plurality of pre-set risk factors affecting major disease factors from a database including factor control research information (S200').

The onset risk factor is a factor that affects the current health status of a member and causes a loss of tissue productivity, and may include antecedent disease factors and health-influencing factors. Antecedent disease factors do not directly cause loss of productivity, but are factors that can increase the likelihood of developing major disease factors in the future. Health influencing factors are factors for lifestyle and environment that influence major and predisposing disease factors. In another embodiment of the present invention, the preceding disease factors may include diabetes, hypertension, obesity, and high cholesterol, and the health-influencing factors may include smoking, drinking, lack of exercise, general stress, and job stress.

Next, the member's attendance information according to each major disease factor is provided (S300'). Since this is the same as the step ( S300 ) of receiving time and attendance information of a member according to each major disease factor according to an embodiment of the present invention, a description thereof will be omitted.

Next, a productivity loss prediction model for predicting the productivity loss of the organization is derived using the prevalence information for each major disease factor, attendance information of members, and disease risk factor information (S400'). The difference from the step (S300) of deriving a productivity loss predictive model according to an embodiment of the present invention is that a productivity loss predictive model is derived by further using the onset risk factor information. By including the risk factors affecting the major disease factors in constructing the productivity loss prediction model, the effect of the member's current health status on the potential productivity loss risk of the organization can be predicted.

An example of a productivity loss prediction model according to another embodiment of the present invention is shown in FIG. 7 . As shown in FIG. 7 , disease risk factors may affect major disease factors, and major disease factors may cause tissue productivity loss. In addition, the risk factors for the onset may include antecedent disease factors and health influencing factors. Antecedent disease factors may affect major disease factors, and health-influencing factors may directly affect major disease factors or may indirectly affect major disease factors by influencing antecedent disease factors. That is, the productivity loss prediction model according to the present invention can predict the productivity loss of the organization based on the member's current health status by deriving a relationship between the member's major disease factors and disease risk factors and the tissue's productivity loss.

In addition, the step of deriving a productivity loss predictive model according to an embodiment of the present invention may derive a productivity loss predictive model by reflecting the path in which the disease risk factor affects the productivity loss.

Fig. 8 shows an example in which all pathways in which the disease risk factors operate in the productivity loss prediction model are shown. 8 shows obesity as an onset risk factor (preceding disease factor).

First, obesity affects depressive disease, which is a major disease factor, and diabetes, which is another antecedent disease (the first and second pathways). And obesity interacts with cardiovascular disease, a major disease factor (3rd and 4th pathways). Diabetes affected by obesity affects cardiovascular disease (path 5), and cardiovascular disease and depressive disease influence each other (path 6 and 7). And finally, depressive and cardiovascular diseases affect productivity loss (paths 8 and 9).

Although obesity is shown as an antecedent disease factor in FIG. 8 , all pathways operating in the productivity loss prediction model can be shown in the same manner for other antecedent disease factors other than obesity. Also, in addition to the antecedent disease factors, productivity loss can also represent all pathways operating in the predictive model for health-affecting factors such as smoking and drinking. That is, the productivity loss prediction model according to the present invention reflects all pathways in which a member's disease risk factors affect other disease risk factors or major disease factors, ultimately affecting the tissue productivity loss, thereby improving the health of the members. The relationship between state and organizational productivity loss can be derived organically.

Next, as shown in FIG. 9 , the step of deriving a productivity loss prediction model (S400') is a step of calculating the sum of changes in the net incidence of major disease factors due to the onset risk factors (S410'), the major disease factors The step of calculating the effect of the onset risk factor on the prevalence of the major disease factor from the sum of the changes in the star prevalence and the net incidence rate (S420'). From the effect of the onset risk factor on the prevalence of the major disease factor, the onset risk factor affects productivity loss. The method may further include calculating the weight of the influence (S430') and calculating the risk index from the weight of the influence of the disease risk factor on the productivity loss (S440').

First, the sum of changes in the net incidence of major disease factors due to the onset risk factors is calculated (S410'). The sum of changes in net incidence of major disease factors due to risk factors may mean the sum of changes in all pathways in which onset risk factors affect productivity loss, as described above. The sum of changes in the net incidence of major disease factors due to the onset risk factors may be calculated by Equation 7 below.

[Equation 7]

Sum of changes in net incidence of major disease factor i due to risk factor j

where i is the major disease factor, j is the risk factor for the onset, nji is the net incidence of the major disease factor i by the risk factor j, p _j is the prevalence of the risk factor j,

is the incidence of major disease factor i among members without major disease factor i among members without risk factor j, OR ⁱ is the odds ratio to the incidence rate

is the odds ratio of the incidence rate for the major disease factor i of the risk factor j,

is the recovery rate of the major disease factor i of members without the risk factor j among members with the major disease factor i, and OR ^r is the odds ratio for the recovery rate

is the odds ratio of the recovery rate for the major disease factor i of the onset risk factor j, and pi is the prevalence of the major disease factor i. In addition, the odds ratio is a value for determining the causal relationship between the independent variable and the dependent variable in a factor control study. In the present invention, the independent variable can be set as an onset risk factor and the dependent variable can be set as a major disease factor. An example of an odds ratio used in a method for evaluating a tissue health index according to another embodiment of the present invention is shown in FIG. 10 .

That is, the sum of the changes in the net incidence of major disease factors due to the onset risk factors may mean the effect of the onset risk factors on the changes in the incidence rates of the major disease factors affecting productivity loss.

As described above, the risk factors for invention include antecedent disease factors and health-influencing factors. Therefore, by replacing the onset risk factor with the preceding disease factor or health-influencing factor in Equation 7, the sum of changes in the net incidence rate of the major disease factor due to the preceding disease factor or the net incidence rate of the major disease factor due to the health-influencing factor It is also possible to calculate the sum of the changes individually. In this case, the antecedent disease factor may be expressed as k, and the health influence factor may be expressed as l.

Meanwhile, Equation 7 can be derived from Equation 8 below.

[Equation 8]

where ni is the net incidence of major disease factor i, Ii is the incidence of major disease factor i, Ri is the recoverer of major disease factor i, P is the total population, Pi is the prevalence of major disease factor i, and , i _i is the incidence of major disease factor i, ri is the recovery rate of major disease factor i,

is the initial value, and ' means the changed value.

Next, the effect of the onset risk factor on the prevalence of the major disease factor is calculated from the sum of the changes in the prevalence and net incidence for each major disease factor (S420'). The effect of the onset risk factors on the prevalence of major disease factors may be calculated by Equation 9 below.

[Equation 9]

Effect of risk factor j on prevalence of major disease factor i

That is, the effect of the onset risk factor on the prevalence of the major disease factor may be expressed as an event in which the major disease factor is newly onset due to the onset risk factor.

Next, the weight of the effect of the onset risk factor on the productivity loss is calculated from the effect of the onset risk factor on the prevalence of the major disease factor (S430'). The weight of the effect of the disease risk factor on productivity loss may be calculated by Equation 10 below.

[Equation 10]

Weight of the effect of risk factor j on productivity loss werTLP _j

Here, LPi denotes the effect on productivity loss due to the major disease factor i, derived from Equation 1 above.

Next, the disease risk index is calculated from the weight of the effect of the disease risk factor on the productivity loss (S440'). The incidence risk index may be calculated by Equation 11 below.

[Equation 11]

risk index

The onset risk index is a value that considers the weight of the effect of the onset risk factor on the loss of productivity and the prevalence of the onset risk factor. The potential risk that the onset risk factor can affect the major disease factors and ultimately cause a loss of tissue productivity. It is an indicator that quantifies and indicates factors. Based on this, it is possible to predict the potential risk of loss of productivity in the organization based on the current health status of the members. In addition, as described above, the onset risk factors include both antecedent disease factors and health-influencing factors, and the antecedent disease risk index based on the antecedent disease factors and the health-affecting risk index based on the health-influencing factors may be calculated, respectively. there is.

In addition, as described above, in the database including the factor control study information, information on the prevalence of major disease factors, etc. may be categorized for each organization. Based on this, the degree of productivity loss between tissues and the risk index of disease can be visualized and expressed as a graph.

Specifically, when productivity loss due to major disease factors does not occur (that is, based on Per LPm = 0, the calculated loss of productivity of the corresponding tissue is scored as a standard score and this can be expressed as an absolute loss of productivity. Based on the maximum and minimum values of the absolute productivity loss of other organizations in the database including the control study information, the calculated productivity loss degree of the corresponding organization may be standardized and expressed as a relative productivity loss degree.

The same method can be applied to the incidence risk index. That is, based on a case in which productivity loss due to major disease factors does not occur, the calculated disease risk index of the corresponding tissue may be standardized and expressed as an absolute disease risk index. In addition, based on the maximum and minimum values of the absolute risk indices of other tissues in the database including factor control research information, the calculated risk index of the disease may be standardized and expressed as a relative risk index.

Next, a tissue health index evaluation method according to another embodiment of the present invention will be described with reference to FIG. 11 .

11 is a graph comparing the degree of productivity loss and the incidence risk index between tissues, and FIG. 11 ( a ) shows the absolute productivity loss degree and the absolute incidence risk index, and FIG. 11 ( b ) shows the relative productivity loss degree and the relative incidence risk index. . 11 shows the productivity loss and disease risk index of the entire organization in the database including organization A, B, and C and factor control study information, respectively. For reference, the absolute productivity loss, relative productivity loss, absolute disease risk index, and relative disease risk index shown in FIG. 11 are each standard-scored values, and in order to intuitively represent the current state of the tissue, the greater the value, the greater the productivity loss. The degree or risk index was shown to be low (ie, the closer to the upper right corner in FIG. 11, the lower the productivity loss).

Specifically, as shown in Figure 11 (a), tissue A has a low absolute productivity loss, so productivity loss according to the member's current health status is high, but the absolute risk index is higher than the average, so future productivity loss will be lower. predicted In addition, organization B is below average in both absolute productivity loss and absolute risk index. In addition, in tissue C, both the absolute productivity loss and absolute risk index are higher than the average, so low productivity loss is predicted not only now but also in the future.

In addition, as shown in FIG. 11( b ), tissue A shows the lowest relative productivity loss compared to other companies, but has a higher relative risk index than tissue B, and is predicted to have lower productivity loss than tissue B in the future. Conversely, organization B shows the lowest relative risk index compared to other companies, so it is predicted that productivity loss will be higher than other companies in the future. On the other hand, organization C has both a higher relative productivity loss and a higher relative risk index than other companies, so it is expected to maintain a lower productivity loss compared to other companies now and in the future. In this way, by calculating the absolute and relative values for the productivity loss degree and the disease risk index, respectively, the current productivity loss degree and the potential productivity loss risk of the organization can be compared with those of other companies based on the member's current health status. .

In another embodiment, based on a database including factor control study information, the calculated productivity loss and disease risk index may be categorized. For example, as described above, the members may be divided into four population groups according to gender based on the age of 40, and the disease risk index may be individually calculated for each population group.

In another embodiment, a weighted average may be calculated in consideration of the number of members for each corresponding population group within an organization, and this may be set as an outbreak risk index. This allows us to understand the health status of each population group within an organization and predict the potential risk of loss of productivity. That is, it is possible to identify a group in an organization that is vulnerable to loss of health and productivity according to gender and age. This can be equally applied to the degree of productivity loss.

As described above, the tissue health index evaluation method and the computer program executing the same according to the embodiments of the present invention, the major disease factors of the member in relation to a plurality of major disease factors preset from a database including factor control study information Productivity loss for receiving prevalence information for each disease factor and time and attendance information of the member according to each of the major disease factors, and predicting the degree of productivity loss of the tissue using the prevalence information for each major disease factor and time and attendance information of the member It is also possible to derive a predictive model.

In addition, the organizational health index evaluation method and the computer program executing the same according to embodiments of the present invention use both data that can objectively determine the health status of a member and data that can identify the member's working environment, so that productivity loss is also By deriving a predictive model, it is possible to calculate the organizational health index that can organically determine the relationship between the health status of members and the loss of productivity of the organization. predictable.

The embodiment according to the present invention described above may be implemented in the form of a computer program that can be executed through various components on a computer, and such a computer program may be recorded in a computer-readable medium. In this case, the medium may be to store a program executable by a computer. Examples of the medium include a hard disk, a magnetic medium such as a floppy disk and a magnetic tape, an optical recording medium such as CD-ROM and DVD, a magneto-optical medium such as a floppy disk, and those configured to store program instructions, including ROM, RAM, flash memory, and the like.

Meanwhile, the computer program may be specially designed and configured for the present invention, or may be known and available to those skilled in the art of computer software. Examples of the computer program may include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like.

The specific implementations described in the present invention are only examples and do not limit the scope of the present invention in any way. For brevity of the specification, descriptions of conventional electronic components, control systems, software, and other functional aspects of the systems may be omitted. In addition, the connection or connection members of the lines between the components shown in the drawings illustratively represent functional connections and/or physical or circuit connections, and in an actual device, various functional connections, physical connections that are replaceable or additional may be referred to as connections, or circuit connections. In addition, unless there is a specific reference such as "essential" or "importantly", it may not be a necessary component for the application of the present invention.

Therefore, the spirit of the present invention should not be limited to the above-described embodiments, and not only the claims described below, but also all ranges equivalent to or changed from these claims belong to the scope of the spirit of the present invention. will do

The present invention can be applied to a method for evaluating a tissue health index and a computer program-related industry for executing the same.

Claims (17)

1. An organizational health index evaluation method for evaluating an organization's health index according to a member's health status,

   receiving, from a database including factor control study information, the prevalence information for each of the major disease factors of the member with respect to a plurality of preset major disease factors;

   receiving time and attendance information of the member according to each of the major disease factors; and

   Deriving a productivity loss prediction model for predicting the productivity loss of the tissue by using the prevalence information for each major disease factor and the attendance information of the member; including; tissue health index evaluation method.
2. The method of claim 1,

   The member's attendance information according to each of the major disease factors is provided by using the member's questionnaire data, a tissue health index evaluation method.
3. 3. The method of claim 2,

   The step of deriving the productivity loss prediction model is,

   calculating an effect on productivity loss for each major disease factor from the member's questionnaire data; and

   Deriving a productivity loss prediction model for predicting the productivity loss by using the prevalence information for each major disease factor and the productivity loss effect for each major disease factor; including; tissue health index evaluation method.
4. 4. The method of claim 3,

   The questionnaire data is data on Absenteeism and Presenteeism of a member due to the major disease factor, wherein the Absenteeism is the absence or early departure time of the member due to the major disease factor, and the presentation Tism is the degree of loss of productivity of members due to the major disease factor, tissue health index evaluation method.
5. 5. The method of claim 4,

   The productivity loss effect for each major disease factor and the productivity loss degree are calculated by the following Equations (1) and (2), respectively, a tissue health index evaluation method.

   Effect of productivity loss due to major disease factor i

   

   Loss of productivity in organization M

   

   with the proviso that LPAi is (epcentism time for a first preset period due to major disease factor i)/(total working hours during said first period), and LPPi is said first time period due to major disease factor i degree of presentation during the period, and LPAi and LPPi are

   

   , and p _im is the prevalence of major disease factor i in tissue m.
6. 6. The method of claim 5,

   The step of deriving the productivity loss prediction model is,

   The effect of the productivity loss for each major disease factor is set as an absolute effect on the productivity loss of the major disease factor, and the member having the major disease factor relative to the effect of the major disease factor on the productivity loss and the total number of members of the organization. Setting the product of the ratio of the number to the relative influence of the major disease factor on the productivity loss; further comprising, a tissue health index evaluation method.
7. 6. The method of claim 5,

   The step of deriving the productivity loss prediction model is,

   The absolute productivity loss degree of the organization is set by standardizing the productivity loss degree on the basis of a case in which no productivity loss occurs, and the maximum and minimum values of the productivity loss degree of the entire organization in the database including the factor control study information. Setting the relative productivity loss of the tissue by standardizing the productivity loss degree based on the; further comprising, the tissue health index evaluation method.
8. 6. The method of claim 5,

   The step of deriving the productivity loss prediction model is,

   Calculating the total productivity loss, the total production time loss, and the total production wage loss by the following Equations (3) to (5), respectively; further comprising, an organizational health index evaluation method.

   total loss of production of tissue m

   

   total production time loss of tissue m

   

   Total production wage loss of organization m

   

   where Pm is the number of members of organization m, Tm is the average working hours of organization m, and Wm is the average wage of organization m.
9. The method of claim 1,

   The major disease factors include cardiovascular disease, allergic disease, respiratory disease, digestive disease, depressive disease and sleep disease, tissue health index evaluation method.
10. The method of claim 1,

    Further comprising; receiving the risk factor information of the member with respect to a plurality of preset risk factors affecting the major disease factors from a database including the factor control study information;

    The step of deriving the productivity loss prediction model is,

    The step of deriving the productivity loss predictive model further using the onset risk factor information, tissue health index evaluation method.
11. 11. The method of claim 10,

    The step of deriving the productivity loss prediction model is,

    Deriving all pathways in which the onset risk factor affects the productivity loss, and calculating the weight of the impact of the onset risk factor on the productivity loss according to the number of pathways to derive the productivity loss prediction model, Methods for evaluating tissue health index.
12. 12. The method of claim 11,

    The step of deriving the productivity loss prediction model is,

    calculating the sum of changes in net incidence of the major disease factors due to the onset risk factors;

    calculating an effect of the onset risk factor on the prevalence of the major disease factor from the sum of the change in the prevalence and the net incidence rate for each major disease factor;

    calculating a weight of the effect of the onset risk factor on the productivity loss from the effect of the onset risk factor on the prevalence of the major disease factor; and

    Calculating an onset risk index from the weight of the effect of the onset risk factor on the productivity loss; Containing, a tissue health index evaluation method.
13. 13. The method of claim 12,

    The sum total of changes in the net incidence of the major disease factors due to the risk factors, the effect of the onset risk factors on the prevalence of the major disease factors, the weight of the effect of the onset risk factors on the productivity loss, and the risk index is calculated by the following formulas (6) to (9), the tissue health index evaluation method.

    Sum of changes in net incidence of major disease factor i due to risk factor j

    

    

    Effect of risk factor j on prevalence of major disease factor i

    

    Weight of the effect of risk factor j on productivity loss werTLPj

    

    risk index

    

    with the proviso that i is a major disease factor, j is an onset risk factor, n _ji is the net incidence of the major disease factor i by the onset risk factor j, p _j is the prevalence of the onset risk factor j,

    

    is the incidence of major disease factor i among members without major disease factor i, without risk factor j,

    

    is the odds ratio of the incidence rate for the major disease factor i of the risk factor j,

    

    is the recovery rate of major disease factor i of members without risk factor j among members with major disease factor i,

    

    is the odds ratio of the recovery rate for the major disease factor i of the onset risk factor j, and _pi is the prevalence of the major disease factor i. In addition, the odds ratio is calculated from a database including the factor control study information.
14. 14. The method of claim 13,

    The step of deriving the productivity loss prediction model is,

    Set the absolute risk index of the tissue by standardizing the onset risk index based on a case in which no productivity loss occurs, and the maximum and minimum values of the onset risk index of the entire organization in the database including the factor-controlled study information Setting the relative incidence risk index of the tissue by standardizing the onset risk index based on a; further comprising, a tissue health index evaluation method.
15. 13. The method of claim 12,

    The risk factors for the onset include a prior disease factor affecting the major disease factor, and a health-influencing factor affecting the major disease factor or the preceding disease factor,

    The onset risk index includes a prior disease risk index due to the preceding disease factor, and a health impact risk index due to the health influence factor, a tissue health index evaluation method.
16. 16. The method of claim 15,

    The major disease factors include cardiovascular diseases, allergic diseases, respiratory diseases, digestive diseases, depressive diseases and sleep diseases,

    The preceding disease factors include diabetes, hypertension, obesity, and high cholesterol,

    The health-influencing factors include smoking, lack of exercise, general stress, job stress, and drinking, organizational health index evaluation method.
17. A computer program stored in a medium for executing the method of any one of claims 1 to 16 using a computer.

Images