JP7437479B2 - Insurance premium calculation system, insurance premium calculation method, and burden prediction method - Google Patents

Description

The present invention relates to an insurance premium calculation system, an insurance premium calculation method, and a burden prediction method, and more specifically, the present invention relates to an insurance premium calculation system, an insurance premium calculation method, and a burden prediction method. Based on the insurance premium calculation system that calculates insurance premiums suitable for animals, insurance premium calculation methods, and animal breeders (hereinafter referred to as "users"), we provide insurance premium calculation systems that calculate insurance premiums suitable for animals. The present invention relates to a burden prediction method that presents costs.

Pet animals such as dogs, cats, and rabbits, and livestock such as cows and pigs are irreplaceable to humans. In recent years, while the average lifespan of animals raised by humans has increased significantly, animals are more likely to suffer from some kind of injury or disease during their lifetime, and the increasing medical costs borne by their owners have become a problem.

In order to maintain the health of animals, it is important to manage their physical condition through daily diet and exercise, and to quickly respond to complaints. The reality is that breeders only notice that their animals are suffering from the disease when the disease progresses and some externally observable signs appear. Additionally, when the musculoskeletal system is weakened due to calcium or vitamin deficiencies, there are rarely any external signs, and the reality is that abnormalities are only noticed when the animal suffers an injury such as a fracture. .

Therefore, there is a need for a simple method for predicting insurance premiums and financial burdens borne by animal breeders.

Patent Document 1 describes the effect of effectively adjusting or improving the intestinal flora by multiplying bacteria of the phylum Bacteroidetes and decreasing bacteria of the phylum Firmicutes in the intestinal flora. However, there is no disclosure of a method for predicting whether or not an animal will suffer from injury or disease based on data regarding the intestinal flora of the animal.

International publication 2017/094892 pamphlet

Therefore, the present invention provides an insurance premium calculation system, an insurance premium calculation method, and an insurance premium calculation method for calculating insurance premiums suitable for animals based on the correlation between animal diversity data and insurance risks in a simple manner. The purpose is to provide a burden prediction method that presents the financial burden due to injury, disease, etc. of the animal.

As a result of analyzing and considering the diversity data of animals enrolled in pet insurance and the huge amount of data on whether or not the animals have made insurance claims, that is, whether they have injuries or diseases, the inventors found that the intestinal flora of animals The present invention has been completed based on the discovery that it is possible to calculate insurance premiums appropriate for each animal and predict the financial burden (medical expenses) to be borne by the user using the diversity data of animals.

That is, the present invention includes the following [1] to [15].
[1] An acquisition unit that acquires the diversity data of the intestinal flora of the pet pet kept by the user, the diversity data of the intestinal flora, the diversity data of the intestinal flora of the pet, and insurance risk. An insurance premium calculation system comprising: a calculation unit that calculates the insurance risk of a pet pet kept by a user based on the correlation between the user and the user.
[2] The insurance premium calculation system according to [1], wherein the acquisition unit further acquires basic information about a pet kept by the user.
[3] The calculation unit further includes a setting unit that sets a correlation between the intestinal flora diversity data of the pet animal and the insurance risk, and the calculation unit The insurance premium calculation system according to [1] or [2], which calculates the insurance risk of a pet kept by a user based on the correlation set by the user.
[4] The insurance premium calculation system according to [1], wherein the correlation between the intestinal microflora diversity data of the pet and the insurance risk is a negative correlation.
[5] The insurance premium calculation system according to [1], wherein the pet animal's intestinal microflora diversity data is a Shannon index.
[6] The calculation unit corrects the insurance premium calculated based on the basic information of the pet according to the insurance risk derived using the diversity data of the intestinal flora of the pet, The insurance premium calculation system of [2] which calculates insurance premiums.
[7] The calculation unit calculates the insurance premium based on the predicted medical expenses calculated based on the pet's basic information, corrected according to the insurance risk derived using the intestinal microbiota diversity data. [2] The insurance premium calculation system that calculates the following.
[8] The calculation unit multiplies the predicted medical expenses calculated based on the pet's basic information by 0.5 to 2.0 times according to the insurance risk derived using the diversity data of the intestinal flora. The insurance premium calculation system according to [2], which calculates insurance premiums based on the corrected information.
[9] The insurance premium calculation system according to [2], wherein the basic information of the pet is at least the breed and age.
[10] The insurance premium calculation system according to [1] or [2], further comprising a prediction calculation unit that corrects the insurance risk calculated by the calculation unit based on information on food consumed by a pet pet kept by the user. .
[11] A computer calculates the insurance risk of the pet pet kept by the user based on the step of acquiring diversity data of the intestinal flora and the diversity data of the pet kept by the user and the correlation. An insurance premium calculation method that includes steps and steps in order.
[12] The insurance premium calculation method according to [11], further comprising a step of acquiring basic information about a pet kept by the user.
[13] The insurance premium calculation method according to [12], wherein the basic information about the pet is at least the breed and age.
[14] The insurance risk derived in the step of calculating the insurance risk of the pet pet kept by the user, based on the diversity data of the pet pet kept by the user and the correlation, is applied to the pet pet kept by the user. The insurance premium calculation method according to [11] or [12], further comprising a step of correcting based on information on food eaten.
[15] A step of acquiring basic information and intestinal flora diversity data of the pet pet kept by the user, and a step of setting a correlation between the pet animal's intestinal flora diversity data and burden risk. A method for predicting financial burden, comprising the steps of: (1) calculating a burden risk of a pet animal kept by the user based on the diversity data of the pet animal kept by the user and the correlation;

According to the present invention, it is possible to provide an insurance premium calculation system, an insurance premium calculation method, and a burden prediction method.

1 is a schematic diagram of an insurance premium calculation system. FIG. 2 is a diagram illustrating an insurance premium calculation method. It is a model diagram showing the correlation between diversity data (diversity index) and insurance risk. FIG. 3 is a diagram showing the correlation between diversity data and insurance risk. It is a figure showing the result of correlation analysis. FIG. 3 is a diagram showing the correlation between diversity data and insurance risk. FIG. 3 is a diagram showing the correlation between diversity data and insurance risk. FIG. 3 is a diagram showing the correlation between diversity data and insurance risk. FIG. 3 is a diagram showing the correlation between diversity data and insurance risk. FIG. 3 is a diagram showing the correlation between diversity data and insurance risk. FIG. 3 is a diagram showing the correlation between diversity data and insurance risk. FIG. 3 is a diagram showing the correlation between diversity data and insurance risk. FIG. 3 is a diagram showing the correlation between diversity data and insurance risk. FIG. 3 is a diagram showing the correlation between diversity data and insurance risk. FIG. 3 is a diagram showing the correlation between diversity data and insurance risk. FIG. 3 is a diagram showing the correlation between diversity data and insurance risk. It is a figure showing the result of correlation analysis. FIG. 3 is a diagram showing the correlation between diversity data and insurance risk. FIG. 3 is a diagram showing the correlation between diversity data and insurance risk. FIG. 3 is a diagram showing the correlation between diversity data and insurance risk. FIG. 3 is a diagram showing the correlation between diversity data and insurance risk. FIG. 3 is a diagram showing the correlation between diversity data and insurance risk. It is a figure showing the relationship between the number of foods and diversity data. It is a figure showing the relationship between the number of foods and diversity data.

[Insurance premium calculation system]
The insurance premium calculation system of the present invention includes an acquisition unit that acquires diversity data of intestinal bacterial flora, and a data collection unit that acquires diversity data of pets kept by the user based on the diversity data of the pets kept by the user and the correlation. A calculation section for calculating insurance risk. In the insurance premium calculation system of the present invention, it is preferable that the acquisition unit further acquires basic information about a pet kept by the user. Moreover, it is preferable that the insurance premium calculation system of the present invention further includes a setting unit that sets a correlation between the intestinal microbiota diversity data of the pet and the insurance risk.

[System overview]
FIG. 1 is a diagram illustrating an overview of an insurance premium calculation system according to an embodiment of the present disclosure. As shown in FIG. 1, the insurance premium calculation system according to this embodiment includes a server 1 and a user terminal 2. Server 1 and terminal 2 are connected via a network. The server 1 includes a processing unit (CPU) 10, a storage unit 20, and an interface unit 30. Note that users in the present invention include not only owners of pet animals, but also agents, breeders, and the like. Pet animals include dogs, cats, rabbits, birds, reptiles, and amphibians, with dogs, cats, and rabbits being preferred.

Further, the processing calculation unit (CPU) 10 includes a calculation unit 11 and a prediction calculation unit 12, the storage unit 20 includes at least a setting unit 21, and the interface unit 30 includes an acquisition unit 31 and an output unit 32. be done. The setting unit 21 may be configured to store formulas, functions, tables, or software related to the correlation between intestinal flora diversity data and insurance risk. In the case of such a configuration, the calculation unit 11 calls the formula, function, table, or software stored in the setting unit 21 based on the basic information of the pet animals kept by the user, and calculates the variety of pets kept by the user. Insurance risk can be calculated based on gender data. Note that insurance risk is an index indicating the high probability of occurrence of an event for which an insurance claim will be paid. Events for which insurance claims are payable are injuries and diseases under animal insurance. Insurance risk is reflected in insurance premiums; the higher the risk, the higher the premium, and the lower the risk, the lower the premium. Note that the insurance premium is the money that the user pays to the insurance company, and the insurance money is the money that the insurance company pays to the user when an event occurs.

Here, the calculation unit 11 calculates the insurance risk of the pet kept by the user based on the diversity data of the pet kept by the user and the correlation. The embodiment of FIG. 1 further includes a prediction calculation section 12. The prediction calculation section 12 predicts the future insurance risk of the pet pet kept by the user based on the information on the food given to the pet. Calculate insurance risk by making informed adjustments.
Further, the setting unit 21 sets the correlation between the diversity data of the intestinal flora of the pet and the insurance risk.
Further, the acquisition unit 31 acquires the diversity data of intestinal flora, preferably basic information of the pet kept by the user, and the output unit 32 outputs the insurance risk, insurance premium, and information calculated by the calculation unit 11. Send the burden amount etc. to the user.

[Diversity data]
Diversity data is data related to the diversity of bacteria in the intestinal flora of animals. The high diversity of the intestinal flora means that the intestinal flora contains a wide range of different types of bacteria evenly. There are several types of diversity data indicators, so-called diversity indexes, and in the present invention, any known one may be used. Diversity indexes include the Shannon-Wiener diversity index (hereinafter sometimes abbreviated as "Shannon index"), the Simpson index, the number of unique sequences detected by a sequencer (amplicon sequence variant: ASV), and OTU (operational taxonomic unit), Faith's PD, Pielou's eveness, etc.

[Measurement of diversity data]
To measure the diversity data of the intestinal flora, known metagenomic analysis methods such as amplicon sequencing and shotgun sequencing using a sequencer such as NGS or bacterial flora analysis methods can be used. For example, a method in which a sample such as feces is collected from an animal, and the DNA and RNA base sequence information of all the organisms contained in the sample is analyzed using a next-generation sequencer, thereby identifying the organisms contained in the sample. can be mentioned. Preferably, all or part of the 16S rRNA gene contained in the sample is amplified as necessary, sequenced, and the obtained sequence is analyzed using software to obtain composition data of bacteria in the sample. There are several methods.

An example of 16S rRNA gene amplicon analysis (meta-16S analysis) using NGS (next generation sequencer) will be specifically explained. First, DNA is extracted from a sample using a DNA extraction reagent, and the 16S rRNA gene is amplified from the extracted DNA by PCR. Thereafter, the base sequences of the amplified DNA fragments are comprehensively determined using NGS, low-quality reads and chimeric sequences are removed, and then the sequences are clustered to perform OTU (Operational Taxonomic Unit) analysis. An OTU is an operational classification unit for treating sequences that have a certain degree of similarity (for example, 96 to 97% or more homology) as one bacterial species. Therefore, the number of OTUs represents the number of bacterial species constituting the bacterial flora, and the number of reads belonging to the same OTU is considered to represent the relative abundance of that species. Furthermore, by selecting a representative sequence from among the number of reads belonging to each OTU, it is possible to identify the family name and genus/species name by searching the database. In this way, the number of bacterial species belonging to a particular family or phylum can be determined. Furthermore, analysis using ASV (Amplicon Sequence Variant) is also possible. Because ASVs are created after removing erroneous sequences generated during PCR and sequencing, single-base sequence variations can be distinguished, allowing for more precise identification. The insurance premium calculation system of the present invention may use diversity data measured in advance, or may receive a fecal sample from a user, measure diversity data, and use the diversity data. When using pre-measured diversity data, the user, for example, sends a fecal sample to an intestinal bacteria analysis company, requests measurement of the intestinal flora, and receives the diversity data from the requested analysis company. receive. The user can transmit the diversity data thus received to the insurance premium calculation system of the present invention through the user terminal. Further, the configuration may be such that the requested analysis company sends the diversity data to the insurance premium calculation system of the present invention on behalf of the user.

Next, technical features of the present invention will be explained in detail.
[Calculation of insurance risk based on diversity data]
An example of a method for calculating insurance risk based on diversity data will be described below with reference to FIG. 3 (model diagram). FIG. 3 is a graph for explaining an example of an insurance risk calculation method based on diversity data according to the present embodiment. The vertical axis of this graph is insurance risk, and the horizontal axis is diversity data. Specifically, when the diversity index (Shannon index) is 3.5 (D1), the insurance risk (R1) is 1. This means that the probability that a pet animal with a diversity index of 3.5 will suffer from an injury or disease is the same as the average of all pet animals. In other words, if the average treatment cost for all pets is 5,000 yen/month, the treatment cost (paid by the owner) for a pet with a diversity index of 3.5 will also be 5,000 yen/month. means. On the other hand, when the diversity index is 3 (D2), the insurance risk (R2) is 1.2. This means that the treatment cost for a pet with a diversity index of 3.5 would be 6,000 yen.

To explain more specifically, FIG. 8 shows the insurance risk of toy poodles aged 0 to 3 years, with the vertical axis representing the insurance risk and the horizontal axis representing the diversity index (Shannon index). In order to perform statistical analysis, the diversity index was divided into four categories: (1) 2.0 or more, less than 3.0, (2) 3.0 or more, less than 4.0, (3) 4.0 or more, less than 5.0, (4) 5.0 or higher, lower than 6.0). As shown in Figure 8, it can be seen that there is a correlation between diversity data and insurance risk for toy poodles aged 0 to 3 years.

[Settings section]
The server or the storage unit may include a setting unit that sets a correlation between the pet animal's intestinal flora diversity data and insurance risk. The correlation here is information indicating a correspondence between the degree of diversity data and the degree of insurance risk. Correlation may be viewed as a model (function) with diversity data as input and insurance risk as output. For example, a correlation is established by statistically processing the diversity data of a plurality of pet animals and the cost of injuries and illnesses and treatment of the pet animals. Note that the pet animal used for setting the correlation and the pet animal bred by the user who is the subject of insurance risk calculation are, in principle, different individuals.

Further, the correlation may be set based on basic information of the pet pet kept by the user. Note that basic information includes age and breed. For example, the server statistically processes the diversity data of pets having basic information similar to the basic information of the pets kept by the user, and the costs of injuries and illnesses of the pets and treatment. Set up correlation. Correlations for each category of basic information (for example, age, breed, etc.) may be constructed in advance in the server, and correlations for categories corresponding to the basic information of pet animals kept by the user may be set. The setting section does not need to set the correlation every time the system is run, and the calculation section may continue to use the correlation once set by the setting section. Alternatively, the correlation may be set each time data related to insurance risk and diversity data is sequentially updated.

By setting the correlation between diversity data and insurance risk, it becomes possible to calculate the insurance risk and insurance premium based on the diversity data of pets kept by the user.

[Insurance premium calculation method]
FIG. 2 is a diagram illustrating an overview of an insurance premium calculation method according to an embodiment of the present disclosure.
As shown in FIG. 2, the insurance premium calculation method according to the present embodiment includes a step S1 of acquiring basic information about the pet pet kept by the user and diversity data of the intestinal flora, and step S2 of setting a correlation between the diversity data of the pet animals kept by the user and the insurance risk; and the computer calculates the insurance risk of the pets kept by the user based on the diversity data of the pets kept by the user and the correlation. step S3 of calculating.

Here, it is preferable that the method further includes a step of acquiring basic information about the pets kept by the user, and it is preferable to set the correlation based on the basic information about the pets kept by the user. By setting the correlation according to the basic information of the pet pet kept by the user, it is possible to calculate an insurance premium that is more suitable for the pet animal. It is easier to first calculate a provisional insurance premium based on basic information, then modify the provisional insurance premium by taking insurance risks based on diversity data into account and calculate the final insurance premium. preferable. In this way, when modifying the provisional insurance premium based on basic information by taking into account the insurance risk based on diversity data, it is preferable to modify the provisional insurance premium by a factor of 0.5 to 2.0. . The inventors have found that, depending on diversity data, the insurance risk of individual pet animals varies between 0.5 and 2.0 times the average. In addition, we calculate insurance premiums based on the medical costs obtained by first calculating the expected medical costs based on basic information, and then modifying the medical costs according to the insurance risk derived based on the diversity data. It's okay.

Furthermore, the method may further include a step of predicting future insurance risks of the pet pet kept by the user based on information on food eaten by the pet pet kept by the user. Since the food that your pet eats increases diversity, future insurance premiums can be calculated by taking food information into account.

Note that the insurance premium is generally the sum of the animal's medical expenses multiplied by the insurance company's burden rate, plus the insurance company's fees, operating costs, etc. Therefore, it is possible to predict the financial burden (medical expenses) that the user will bear in the same way that the insurance premium calculation system calculates insurance premiums.

Examples of the present invention will be shown below. The present invention is not limited to the following examples.
[Example 1]

(Selection of dog)
Insurance risks were investigated based on insurance claims made during the same period for approximately 110,000 individuals whose intestinal microbiota diversity index was measured from fecal samples during the insurance contract period (one year). Since the presence or absence of injury or disease before the collection of the fecal sample is not taken into account, individuals suffering from injury or disease before the collection of the sample are also included.

(DNA extraction from fecal sample)
Fecal samples were collected from each dog and DNA was extracted as follows.
Dog fecal samples were collected by dog owners using a fecal collection kit. The fecal samples were received and suspended in fixative (10% EtOH, 1.07% NH4Cl, 5mM EDTA, 0.09% NaN3).
Next, 200 uL of the fecal suspension and 810 uL of Lysis buffer (containing 224 ug/mL Protenase K) were added to the bead tube, and the beads were crushed using a bead homogenizer (6,000 rpm, crushing 20 seconds, interval 30 seconds, crushing for 20 seconds). Thereafter, the specimen was left to stand on a heat block at 70°C for 10 minutes to perform treatment with Proteinase K, and subsequently, the sample was left to stand on a heat block at 95°C for 5 minutes to inactivate Proteinase K. DNA was automatically extracted from the lysed specimen using Chemagic 360 (PerkinElmer) according to the Chemagic kit stool protocol to obtain 100 uL of DNA extract.

(Meta-16S RNA gene sequence analysis)
Meta-16S sequence analysis was performed using a modified illumina 16S Metagenomic Sequencing Library Preparation (version 15044223 B). First, a 460 bp region containing variable regions V3-V4 of the 16S rRNA gene was amplified by PCR using universal primers (Illumina_16S_341F and Illumina_16S_805RPCR). The PCR reaction mixture consisted of 10 uL of DNA extract, 0.05 uL of each primer (100 uM), 12.5 uL of 2x KAPA HiFi Hot-Start ReadyMix (F. Hoffmann-La Roche, Switzerland), and 2.4 uL of PCR grade water. It was prepared by For PCR, after heat denaturation at 95°C for 3 minutes, a cycle of 95°C for 30 seconds, 55°C for 30 seconds, and 72°C for 30 seconds was repeated 30 times, and finally an extension reaction was performed at 72°C for 5 minutes. The amplified products were purified using magnetic beads and eluted with 50 uL of Buffer EB (QIAGEN, Germany). The purified amplification product was subjected to PCR using Nextera XT Index Kit v2 (illumina, CA, US) and an index was added. A PCR reaction solution was prepared by mixing 2.5 uL of amplification product, 2.5 uL of each primer, 12.5 uL of 2x KAPA HiFi Hot-Start ReadyMix, and 5 uL of PCR grade water. For PCR, after heat denaturation at 95°C for 3 minutes, a cycle of 95°C for 30 seconds, 55°C for 30 seconds, and 72°C for 30 seconds was repeated 12 times, and finally an extension reaction was performed at 72°C for 5 minutes. The indexed amplification products were purified using magnetic beads and eluted with 80-105 uL of Buffer EB. The concentration of each amplification product was measured using a NanoPhotometer (Implen, CA, US), adjusted to 1.4 nM, and then mixed in equal amounts to form a library for sequencing. The DNA concentration of the sequencing library and the size of the amplified product were confirmed by electrophoresis, and this was analyzed by MiSeq. For analysis, 2×300 bp paired-end sequencing was performed using MiSeq Reagent Kit V3. The obtained sequence was analyzed using analysis software called QIIME2 to obtain bacterial composition data.
The sequences of the universal primers used above are as follows. This universal primer can be purchased commercially.
Illumina_16S_341F
5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG- 3'

llumina_16S_805R
5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC- 3'

According to the above method, composition data of intestinal flora was obtained for all dog breeds from 0 years old to 3 years old, and diversity index (Shannon index, ASV index) was measured using QIIME2. Since outliers were excluded, the effective number of individuals was 105,335 individuals for the Shannon index and 105,677 individuals for the ASV index.

(Confirmation of injury/illness/insurance claims)
We investigated whether there were any insurance claims made during the insurance period for all of the dog breeds listed above. As mentioned above, since the presence or absence of injury or disease before the collection of the fecal sample is not taken into account, individuals suffering from injury or disease before the collection of the sample are also included.

(Correlation diagram)
The correlation between the diversity data obtained in the above experiment and insurance risk is shown in Figures 4 and 6 to 13. The vertical axis is insurance risk, and the horizontal axis is diversity index.
Figure 5 shows the results of the correlation analysis of Figure 4 (dog breeds not limited, 0-3 years old), and it can be seen that there is a correlation between diversity data and insurance risk.
Correlation coefficient: -0.97 (negative correlation)
Approximate formula: y = -0.2078x + 1.8795,
Coefficient of determination: ^R2 = 0.9438

In order to explain that a correlation can be shown even in dogs other than those over 0 years old and under 3 years old, we investigated correlations under other conditions (Figures 14 to 16).
FIG. 14 is a correlation diagram between diversity data and insurance risk for all dog breeds (136,033 individuals) aged 0 to 3 years old. These results show that there is a correlation between diversity data and insurance risk not only for children aged 0 and over and under 3, but also for children aged 4 and over.
Moreover, FIG. 15 is a diagram in which the diversity index is changed to the ASV index instead of the Shannon index (105,677 individuals). It can be seen that a correlation exists between diversity data and insurance risk even if the diversity index is changed.
Furthermore, FIG. 16 is a correlation diagram between diversity data and insurance risk for all cat species (36,312 individuals) aged 0 to 3 years old. It can be seen that there is a correlation between diversity data and insurance risk not only for dogs but also for other pet animals. Note that the diversity of cats will be explained in detail in Examples 3 and 4.

(simulation)
First of all, the average medical expenses for a dog aged 0-3, regardless of breed, is 100,000 yen per year, and an animal insurance product with 50% coverage of 70,000 yen per year (including 20,000 yen in commissions, operating costs, etc.) Assume that there exists. Here, we simulate presenting an insurance premium to Shiba Inu A, who is 2 years old and has diversity data of 4.3.
First, a correlation between "Shiba, 0-3 years old" is established based on age and breed (Figure 10). Here, since Shiba Inu A has diversity data of 4.3, the insurance risk is calculated to be approximately 0.9. Therefore, Shiba Inu A's medical expenses are predicted to be 90,000 yen. Since this is an insurance product with 50% coverage, a total of 65,000 yen is offered, which is 45,000 yen plus 20,000 yen for commissions, operating costs, etc.

[Example 2]
As a result of our research, when comparing Pomeranians who ate only one type of commercially available dry food with Pomeranians who ate a combination of two types of dry food, the latter had a diversity index (Shannon index) of approximately 0. 2 (4.2 → 4.4). In other words, by adding the information that a Pomeranian who had been eating only one type of dry food has started eating two types of dry food together, the diversity index increases by about 0.2, and the future insurance risk becomes 0. A decrease of about .1 can be expected (Figure 13). When applied to the above simulation, it is possible to present to the user that medical expenses will be reduced by about 10,000 yen and insurance premiums will be reduced by about 5,000 yen.

[Example 3]

(Cat selection)
Insurance risks were investigated based on insurance claims made during the same period for approximately 110,000 individuals whose intestinal microbiota diversity index was measured from fecal samples during the insurance contract period (one year). Since the presence or absence of injury or disease before the collection of the fecal sample is not taken into account, individuals suffering from injury or disease before the collection of the sample are also included.

(DNA extraction from fecal sample)
Fecal samples were collected from each cat and DNA was extracted as follows.
Cat fecal samples were collected by cat owners using a fecal collection kit. The fecal samples were received and suspended in fixative (10% EtOH, 1.07% NH4Cl, 5mM EDTA, 0.09% NaN3).
Next, 200 uL of the fecal suspension and 810 uL of Lysis buffer (containing 224 ug/mL Protenase K) were added to the bead tube, and the beads were crushed using a bead homogenizer (6,000 rpm, crushing 20 seconds, interval 30 seconds, crushing for 20 seconds). Thereafter, the specimen was left to stand on a heat block at 70°C for 10 minutes to perform treatment with Proteinase K, and subsequently, the sample was left to stand on a heat block at 95°C for 5 minutes to inactivate Proteinase K. DNA was automatically extracted from the lysed specimen using Chemagic 360 (PerkinElmer) according to the Chemagic kit stool protocol to obtain 100 uL of DNA extract.

(Meta-16S RNA gene sequence analysis)
Meta-16S sequence analysis was performed using a modified illumina 16S Metagenomic Sequencing Library Preparation (version 15044223 B). First, a 460 bp region containing variable regions V3-V4 of the 16S rRNA gene was amplified by PCR using universal primers (Illumina_16S_341F and Illumina_16S_805RPCR). The PCR reaction mixture consisted of 10 uL of DNA extract, 0.05 uL of each primer (100 uM), 12.5 uL of 2x KAPA HiFi Hot-Start ReadyMix (F. Hoffmann-La Roche, Switzerland), and 2.4 uL of PCR grade water. It was prepared by For PCR, after heat denaturation at 95°C for 3 minutes, a cycle of 95°C for 30 seconds, 55°C for 30 seconds, and 72°C for 30 seconds was repeated 30 times, and finally an extension reaction was performed at 72°C for 5 minutes. The amplified products were purified using magnetic beads and eluted with 50 uL of Buffer EB (QIAGEN, Germany). The purified amplification product was subjected to PCR using Nextera XT Index Kit v2 (illumina, CA, US) and an index was added. A PCR reaction solution was prepared by mixing 2.5 uL of amplification product, 2.5 uL of each primer, 12.5 uL of 2x KAPA HiFi Hot-Start ReadyMix, and 5 uL of PCR grade water. For PCR, after heat denaturation at 95°C for 3 minutes, a cycle of 95°C for 30 seconds, 55°C for 30 seconds, and 72°C for 30 seconds was repeated 12 times, and finally an extension reaction was performed at 72°C for 5 minutes. The indexed amplification products were purified using magnetic beads and eluted with 80-105 uL of Buffer EB. The concentration of each amplification product was measured using a NanoPhotometer (Implen, CA, US), adjusted to 1.4 nM, and then mixed in equal amounts to form a library for sequencing. The DNA concentration of the sequencing library and the size of the amplified product were confirmed by electrophoresis, and this was analyzed by MiSeq. For analysis, 2×300 bp paired-end sequencing was performed using MiSeq Reagent Kit V3. The obtained sequence was analyzed using analysis software called QIIME2 to obtain bacterial composition data.
The sequences of the universal primers used above are as follows. This universal primer can be purchased commercially.
Illumina_16S_341F
5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG- 3'

llumina_16S_805R
5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC- 3'

According to the above method, composition data of intestinal flora was obtained for all cat species aged 0 to 3 years old, and the diversity index (Shannon index) was measured using QIIME2. In addition, since outliers were excluded, the effective number of individuals for the Shannon index was 36,312 individuals.

(Confirmation of injury/illness/insurance claims)
We investigated whether or not there were any insurance claims made during the insurance period for all of the above cat breeds. As mentioned above, since the presence or absence of injury or disease before the collection of the fecal sample is not taken into account, individuals suffering from injury or disease before the collection of the sample are also included.

(Correlation diagram)
FIG. 16 shows the correlation between diversity data and insurance risk obtained in the above experiment. The vertical axis is insurance risk, and the horizontal axis is diversity index.
Figure 17 shows the results of the correlation analysis of Figure 16 (cat breeds not limited, 0-3 years old), and it can be seen that a correlation exists between diversity data and insurance risk.
Correlation coefficient: -0.93 (negative correlation)
Approximate formula: y = -0.1862x + 2.0147,
Coefficient of determination: ^R2 = 0.8712

In addition, Figure 18 is a diagram showing the correlation between diversity data and insurance risk for (no restrictions on cat breeds, age 0), and Figure 19 shows the correlation for (no restrictions on cat breeds, age 1). FIG. It can be seen that the correlation holds not only for the total of 0-3 years old, but also for each age group.

Furthermore, Figure 20 shows the correlation between diversity data and insurance risk for (Scottish Fold, 0-3 years old), and Figure 21 shows the correlation for (Mixed breed, 0-3 years old). FIG. It can be seen that a correlation holds even when analyzed by variety.

Furthermore, FIG. 22 is a correlation diagram between diversity data and insurance risk for all cat species (45,400 individuals) aged 0 to 7 years old. These results show that there is a correlation between diversity data and insurance risk not only for children aged 0 and over and under 3, but also for children aged 4 and over. Furthermore, it can be seen that there is a correlation between diversity data and insurance risk not only for dogs but also for cats.

(simulation)
First of all, the average medical expenses for cats aged 0-3 years, regardless of breed, is 100,000 yen per year, and an animal insurance product with 50% coverage of 70,000 yen per year (of which 20,000 yen includes commissions, operating costs, etc.) Assume that there exists. Here, a simulation is performed in which an insurance premium is offered to Scottish Fold A, who is 2 years old and has diversity data of 5.8.
First, a correlation between "Scottish Fold, 0-3 years old" is established based on age and breed (Figure 20). Here, since Scottish Fold A has diversity data of 5.8, the insurance risk is calculated to be approximately 0.8. Therefore, Scottish Fold A's medical expenses are predicted to be 80,000 yen. Since this is an insurance product with 50% coverage, a total of 60,000 yen is offered, which is 40,000 yen plus 20,000 yen for fees, operating costs, etc.

[Example 4]
As a result of research by the present inventors, when comparing dogs that ate only one type of food with dogs that ate a combination of two or more types of food, the latter showed an increase in diversity index (Shannon index). Got it (Figure 23). Similar results were obtained for cats (Figure 24). In other words, by adding information that a pet that had been eating one type of food has started eating two or more types of food at the same time, the diversity index will increase according to the number of types of food that it has started eating, and it will It can be expected that the insurance risk will decrease. In other words, it is possible to present to the user how much medical costs will be reduced by feeding the user multiple foods. Note that, in the present invention, information on the brand of the food and the processing method (whether it is dry food or fresh food) is not considered. Most commercially available pet foods are comprehensive nutritional foods, and the impact of food brands and processing methods on the diversity index is compared to the impact of the number of food types on the diversity index. This is because it is small.

[Example 4-1]
When comparing a dog that ate only one type of food with a dog that ate a combination of three types of food, the diversity index (Shannon index) of the latter increased by about 0.025 (Figure 23). By adding the information that Shiba Inu A in Example 1, who had been eating one type of food, started eating three types of food at the same time, it is thought that the diversity of Shiba Inu A would increase from 4.3 to 4.325. By referring to the "Shiba 0-3 years old" correlation (Figure 10), it can be predicted that the insurance risk will decrease from about 0.9 to about 0.89. Therefore, by applying the above simulation, it is possible to present to the user a prediction that medical expenses will decrease by about 1000 yen.

[Example 4-2]
As a result of our research, when we compare cats who eat only one type of commercially available dry food with cats who eat three types of dry food, the diversity index (Shannon index) of the latter increases by approximately 0.03. (Figure 24). In other words, by adding the information that Scottish Fold A, who had been eating only one type of dry food, has started eating three types of dry food, the diversity of Scottish Fold A increases from 5.8 to 5.83. Therefore, by referring to the correlation of "Scottish Fold 0-3 years old" (Figure 20), it can be predicted that the insurance risk will decrease from 0.8 to 0.79. Therefore, by applying the above simulation, it is possible to present to the user a prediction that medical expenses will decrease by about 1000 yen.

Claims (15)

an acquisition unit that acquires diversity data of the intestinal flora of pets kept by the user;
a calculation unit that calculates the insurance risk of the pet pet kept by the user based on the intestinal flora diversity data and the correlation between the intestinal flora diversity data of the pet animal and the insurance risk;
An insurance premium calculation system comprising:
The acquisition unit further acquires basic information of a pet kept by the user,
The calculation unit corrects the insurance premium calculated based on the basic information of the pet according to the insurance risk derived using the diversity data of the intestinal flora of the pet, thereby reducing the insurance premium. An insurance premium calculation system that calculates an acquisition unit that acquires diversity data of the intestinal flora of pets kept by the user;
a calculation unit that calculates the insurance risk of the pet pet kept by the user based on the intestinal flora diversity data and the correlation between the intestinal flora diversity data of the pet animal and the insurance risk;
An insurance premium calculation system comprising:
The acquisition unit further acquires basic information of a pet kept by the user,
The calculation unit calculates the insurance premium based on the predicted medical expenses calculated based on the pet's basic information, corrected according to the insurance risk derived using the diversity data of the intestinal flora. An insurance premium calculation system. an acquisition unit that acquires diversity data of the intestinal flora of pets kept by the user;
a calculation unit that calculates the insurance risk of the pet pet kept by the user based on the intestinal flora diversity data and the correlation between the intestinal flora diversity data of the pet animal and the insurance risk;
An insurance premium calculation system comprising:
The acquisition unit further acquires basic information of a pet kept by the user,
The calculation unit has revised the predicted medical expenses calculated based on the pet's basic information by 0.5 to 2.0 times according to the insurance risk derived using the diversity data of the intestinal flora. An insurance premium calculation system that calculates insurance premiums based on. an acquisition unit that acquires diversity data of the intestinal flora of pets kept by the user;
a calculation unit that calculates the insurance risk of the pet pet kept by the user based on the intestinal flora diversity data and the correlation between the intestinal flora diversity data of the pet animal and the insurance risk;
An insurance premium calculation system comprising:
An insurance premium calculation system further comprising a prediction calculation unit that corrects the insurance risk calculated by the calculation unit based on information on food ingested by a pet pet kept by the user. The calculation unit further includes a setting unit that sets a correlation between the intestinal flora diversity data of the pet and the insurance risk, and the calculation unit calculates the correlation between the intestinal flora diversity data and the insurance risk set by the setting unit. 5. The insurance premium calculation system according to claim 1 , wherein the insurance premium calculation system calculates the insurance risk of a pet kept by the user based on the correlation. 5. The insurance premium calculation system according to claim 1, wherein the correlation between the intestinal flora diversity data of the pet and the insurance risk is a negative correlation. The insurance premium calculation system according to any one of claims 1 to 4, wherein the pet animal's intestinal bacterial flora diversity data is a Shannon index. The insurance premium calculation system according to any one of claims 1 to 4 , wherein the basic information about the pet is at least a breed and an age. obtaining gut microbiota diversity data;
a step in which the computer calculates the insurance risk of the pet pet kept by the user based on the correlation between the diversity data of the pet pet kept by the user, the diversity data of the intestinal flora of the pet animal, and the insurance risk;
An insurance premium calculation method comprising in order :
Further, the computer includes a step of acquiring basic information of a pet kept by the user,
a step in which the computer calculates the insurance risk of the pet pet kept by the user based on the correlation between the diversity data of the pet pet kept by the user, the diversity data of the intestinal flora of the pet animal, and the insurance risk; , a step in which the computer corrects the insurance premium calculated based on the basic information of the pet according to the insurance risk derived using the diversity data of the intestinal flora of the pet;
Insurance premium calculation method. obtaining gut microbiota diversity data;
a step in which the computer calculates the insurance risk of the pet pet kept by the user based on the correlation between the diversity data of the pet pet kept by the user, the diversity data of the intestinal flora of the pet animal, and the insurance risk;
An insurance premium calculation method comprising in order :
Further, the computer includes a step of acquiring basic information of a pet kept by the user,
a step in which the computer calculates the insurance risk of the pet pet kept by the user based on the correlation between the diversity data of the pet pet kept by the user, the diversity data of the intestinal flora of the pet animal, and the insurance risk; , calculating an insurance premium based on the predicted medical expenses calculated based on the basic information of the pet, modified according to the insurance risk derived using the diversity data of the intestinal flora, Insurance premium calculation method. obtaining gut microbiota diversity data;
a step in which the computer calculates the insurance risk of the pet pet kept by the user based on the correlation between the diversity data of the pet pet kept by the user, the diversity data of the intestinal flora of the pet animal, and the insurance risk;
An insurance premium calculation method comprising in order :
Further, the computer includes a step of acquiring basic information of a pet kept by the user,
The computer calculates the insurance risk of the pet pet kept by the user based on the correlation between the diversity data of the pet pet kept by the user, the diversity data of the intestinal flora of the pet animal, and the insurance risk. is based on the predicted medical expenses calculated based on the basic information of the pet, revised by 0.5 to 2.0 times according to the insurance risk derived using intestinal microbiota diversity data. The insurance premium calculation method is the step of calculating the insurance premium. obtaining gut microbiota diversity data;
a step in which the computer calculates the insurance risk of the pet pet kept by the user based on the correlation between the diversity data of the pet pet kept by the user, the diversity data of the intestinal flora of the pet animal, and the insurance risk;
An insurance premium calculation method comprising in order :
Based on the diversity data of the pets kept by the user and the correlation, the insurance risk derived in the step of calculating the insurance risk of the pets kept by the user is applied to the food that the pets kept by the user eat. A method of calculating insurance premiums further comprising the step of making corrections based on the information. The method further includes a step of setting a correlation between the diversity data of the intestinal flora of the pet and the insurance risk, and the diversity data of the pet kept by the user and the diversity data of the intestinal flora of the pet. The computer calculates the insurance risk of the pet pet kept by the user based on the correlation between the intestinal flora diversity data and the insurance risk. 13. The method according to claim 9, wherein the step of setting the correlation between the sex data and the insurance risk is a step of calculating the insurance risk of the pet kept by the user based on the set correlation. Insurance premium calculation method. 13. The insurance premium calculation method according to claim 9, wherein the correlation between the intestinal flora diversity data of the pet and the insurance risk is a negative correlation. The insurance premium calculation method according to any one of claims 9 to 12, wherein the pet animal's intestinal microflora diversity data is a Shannon index.

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