AIG Institute Insight #05

Mar 2020

Potential in “Collaborative Health Plus,” a health data sciencelinked to corporate health insurance～Results and Challenges from the WLAQ Survey～
Manabu Fujii, Executive Fellow, AIG Institute
NOTICE: This represents an English translation of an original report released in Japanese by the AIG Institute in
March, 2020. In the event that there are any inconsistencies between the Japanese version and the English one, theJapanese version shall control and supersede the substance of the English translated version.

Overview
For scientific and statistical research and analysis using various data related to health, as wellas health data science, various types of initiatives are being executed by industry, academia, andgovernment, but, if you are going to take an approach to obtain new health data from scratch, youneed to launch a large-scale research project with plenty of fund to cover cost andcomprehensiveness, which is not easy to implement.
On the other hand, if the approach of utilizing integrated data by acquiring additional data
newly is taken after starting with health data such as health examinations and medicalprescriptions managed by the corporate health insurance society, high-quality, comprehensive
health data can be obtained at a low cost, making it relatively easy to clear these problems.
At the same time, the momentum for "data health" that utilizes medical and health data
managed by the health insurance society as a resource is increasing, and, in particular,"collaborative health" where the health insurance society and employers work together to promote
employee health and prevent diseases for health management is getting more attentions against
the background of strong demand for the complete digitization of health examinations and medicalprescription data, as well as the efficiency of health insurance management.
Because of this, advanced health data science ("collaborative health plus") is expanding, taking
it one step ahead from traditional collaborative health initiatives, for joint researches between
researchers who want to conduct research utilizing low-cost, high-quality health data, andcorporate health insurance societies and employers, who want to use health and medical data
managed intensively to promote employee health and improve management efficiency.
AIG Research Institute has recently conducted a joint research to analyze the relationshipbetween so-called "oversitting" risk and health as a concrete approach to such collaborative healthplus in collaboration with the National Institute of Occupational Safety and Health (herein afterreferred to as "JNIOSH"). and AIG health insurance society (herein after referred to as "AIG
Health Insurance"). More than 2,000 employees participated in the survey, and they were able toobtain sufficient sample count and accuracy data to analyze the relationship between lifestyle andmedical care and health, including "oversitting."
In this paper, the result of a survey we have recently conducted is summarized, including thestructure of the joint research agreement, the handling of sensitive information, obtaining consent
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to data provision, anonymizing data, and preparing various types of internal communication, andother results such as response rates and remaining challenges. We hope that the knowledge
gained from the research will contribute to the development of "collaborative health plus" in the
future.

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Part 1: Collaborative health “plus”
Trend of Health Data Science
Health data science provides a mechanism to solve various health issues related to people's
health, such as health preservation, medical care, and nursing care, through a scientific approachto statistically analyzing data. In recent years, this health data science has attracted attention,and there are two major factors behind this.
One reason is that IT and healthcare technologies have advanced, which makes it possible toaccess more precise and vast amounts of data in the form of electronic data statistically easy toprocess, and computers with the ability to accumulate and analyze that data can be used at a low
cost. This includes technological innovations in artificial intelligence (AI), which has become a hot
topic these days.
And the other is that while the number of elderly people has increased dramatically due to thedeclining birthrate and aging population, the number of workers supporting them has decreased,
and the health insurance and long-term care insurance systems are facing a financial crisis, andurgent issues such as preventing diseases and allowing them to live healthy lives (extending
healthy life expectancy) and improving the efficiency of medical and nursing care infrastructure
are emerging.
In response to the growing needs of health data science, Yokohama City University, a public
university corporation, has established 1 a "Health Data Science Major" at the graduate school
since fiscal year 2020, and has begun to develop data scientists in the health field. Keidanren also
proposed a new era of healthcare that leverages health data in the "Healthcare in the Society 5.0
Era” in 2018. 2 According to them, health care in the future is expected to have the following threecharacteristics. It is fair to say that health data science is a concrete approach to realizing healthcare in this new era.
① Shift to “MI-BYO”, or pre-disease, and disease prevention
The core of health care will shift from "medical care" centered on the healing of diseases tocare and "prevention" at the "pre-disease" stage before the "disease" stage. The scope of"health care" will expand, and the period of time for individuals to live healthy will be longer.
② Personalized healthcare
Uniform treatment for the average patient and symptoms will be changed focusing ongroups of people or individuals. All the individuals will be able to receive prevention, care forpre-disease, treatment, and nursing care at the most appropriate timings.
③ Independent engagement of individuals
Individuals are actively involved in the care of individual health and illness. Individuals use
1 https://www.yokohama-cu.ac.jp/ds/news/fsnbt50000000bpp-att/20190624_hds.pdf
2 https://www.keidanren.or.jp/policy/2018/021.html

visited on Oct 29, 2019

visited on Oct 29, 20193

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IT and data on their own decisions to manage their health over life courses.
Data Health Planning and Collaborative healthIn the "Health Care System Reform Plan" 3 formulated in 2002 as a medium- to long-term
strategy for healthcare reform, the policy to mandate digitizing the medical prescription in the
beginning of fiscal year 2011 was indicated. As a result, almost all medical prescriptions inhealthcare institutions are now electrified. At the same time, it has become common to manage theresults of medical examinations electronically, and the health insurance society has been able toeasily and accurately grasp the health status of society members and the status of medical careand medical expenses using electronic data.
The data health plan is a business plan for effectively and efficiently implementing healthservices in the PDCA cycle in accordance with the analysis of medical examination and medicalprescription data set by the health insurance society based on these changes. It is also regarded as
the health insurance society's medium-term management plan.

PDCA Cycle of Data Health Plan (From the Ministry 4 of Health, Labour andWelfare's Collaborative Health Guidelines for Promoting Data Health and HealthManagement (2017))

3 https://www.kantei.go.jp/jp/kakugikettei/2001/1129syakai.html visited on Dec 19, 20194

"CollaborativeHealth Guidelines for Promoting Data Health and Health Management" released https://www.mhlw.go.jp/stf/houdou/0000170819.html visited on Oct 29, 20194

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The data health plan proposed in the "Japan Revitalization Strategy" announced by thegovernment in 2013 and developed by all health insurance societies has moved to the period II,
which is currently planned as FY 2018-2024, following period I of FY 2015-2017, and it is expectedto achieve more concrete results beyond the period I.
One of the initiatives being promoted by the government in formulating data health plans is"collaborative health."
Collaborative health refers to the facilitation of health promotion initiatives in cooperation withthe employers of the organizations to which the members belong, and the following six points are
required in the "Guidelines for the Implementation of Health Services based on the Health
Insurance Act 5 (Notification No. 308 of the Ministry of Health, Labour and Welfare, July 30,2004)".
① Endeavor to gain an understanding from employers about the necessity of health services byusing objective indicators of the health status and health issues of members.
② Foster a workplace environment that makes it easy to participate in health services after
fully explaining the contents, implementation methods, and expected effects of healthservices to employers, etc.
③ Strive to obtain support from employers, etc. to encourage the members to participate inhealth services, etc.
④ Encourage employers to create an environment in the workplace where it is easy formembers to voluntarily engage themselves in health promotion, such as smoking cessationin the workplace.
⑤ Make sufficient adjustments, including the definitions of roles with employers, etc., andstrive for efficient implementation.
⑥

Strive to actively cooperate with businesses based on the Occupational Safety and Health
Act implemented by employers

In other words, in addition to the management and operation of the health insurance system,
collaborative health is seeking active health maintenance and promotion in cooperation with
employers, as well as involvement in disease prevention. In order to promote collaborative health,the Ministry of Health, Labour and Welfare has established and published "Collaborative Health
Guidelines for Promoting Data Health and Health Management (hereinafter referred to as"Guidelines")”.
As seen from the name of these guidelines, collaborative health is not only part of the data
health initiatives utilizing various data provided by health insurance, but also related to "healthmanagement" that has attracted attention in recent years on the employer's side. It is a concept
5 https://www.mhlw.go.jp/web/t_doc?dataId=84aa6295&dataType=0&pageNo=1 (visited on Dec 16,2019)5

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Mar 2020

that includes the health insurance society's active involvement in health management. In fact, theJapan Health Council, which is responsible for promoting the data health plan, is also anorganization that certifies and awards health management.
Initiative as Collaborative health plus
Currently, AIG Research Institute has concluded 6 a joint research agreement with JNIOSH
together with AIG Health Insurance, and is conducting joint research on workplace healthpromotion initiatives for AIG Group employees.
The "WLAQ Survey" in this paper was conducted as part of the joint research, and employees
were asked to respond to the "oversitting" risk questionnaire (WLAQ) survey, and after receiving
medical and medical prescription data, the two data sets were combined. It is a study to analyze
the relationship between lifestyle (obtained by the questionnaire) such as "oversitting" and healthindicators such as blood pressure and blood glucose level, and annual medical expenses (obtained
from medical prescription data).
This research and analysis will be positioned as a derivative of collaborative health from theperspective of AIG Health Insurance. This is because AIG Research Institute, using the healthexamination and medical prescription data owned, is the hub to raise health awareness andprovide useful health information in cooperation with the employers of individual groupcompanies.
In addition, this study means academic research for JNIOSH to obtain new insights into"oversitting" risks. In addition to the medical examination and medical prescription data managed
by AIG Health Insurance, new survey responses are included, creating new possibilities and added
value beyond the framework of conventional collaborative health.
In this paper, "collaborative health plus" is used to describe the initiative of this type of derived
collaborative health that creates new added value by adding new data to health insurance data incooperation with external research institutions. The significance and potential of this will be
discussed below.
In addition, the practical knowledge and issues obtained through the implementation of thisWLAQ survey will be explained in detail in Part 2, and the definition of "collaborative health plus"
will be explained first.
Collaborative Health Plus Framework
The "collaborative health plus" in this paper is a health data science initiative in which thefollowing three parties work together.
1) Corporate health insurance society2) Employer (human resources, corporate planning, R&D, etc.)
6 http://www-510.aig.co.jp/assets/documents/press-release/2019/2019-05-28-joint-research-on-health-promotion-

ja.pdf (visited on Dec 9th, 2019)6

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3) External research institution
And the data we deal with is as follows:a) Health examination data and medical prescription data managed by the health insurance
societyb) New data acquired through questionnaires, IoT equipment, additional inspections, etc.

a) Medical
examination/pr
escription data

1) health insurance
union“Plus” portion

Provision of medical examination/prescription data

3) External
research
instituteThree-party
collaboration

b)
Newly
Data
acquiredacquisition
data

Combined

Analytics master（Combined
data）
Analysis/
research

2) Employer
Employee

Feedback of research result to
employer/health insurance union

Highly value
addedhealth data
scienceresearch

View of the framework of collaborative health plus
The characteristics of the collaborative health plus are the found between 3) and b) above.
In the health data utilization by normal collaborative health, only the existing medical
examination data is used, and the results of analysis by entrusted outsourcers are shared with theemployer and the employees as a main format, while in the collaborative health plus, direct
analysis of the medical examination data only is not the main objective. Its main objective is togain new knowledge and insights into health by combining additional data (b above) and medical
examination data/medical prescription data.
In addition, the data obtained is assumed to be advanced data with novelty that external
research institutions (3 above) conduct research on or have technology for. By acquiring theadditional data for health insurance society members and combining it with medical
examination/medical prescription data provided by the health insurance society, the research
institutions can analyze how the conditions indicated by the additional data relate to the healthstatus indicated by the medical examination/medical prescription data and the status of medical
consultation medication at hospitals.
The employer (above 2) is responsible for working between research institutions and the healthinsurance society, overseeing and leading the collaborative health plus initiative, and using theresults to promote the health and health of employees.
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Benefits of collaborative health plus
The collaborative health plus, which extends the collaborative health framework and promotes
health data science in collaboration between external research institutions, health insurance
society, and the employer, is expected to receive the following benefits compared to typicalcollaborative health and health data science projects:
1) Benefits for Researchers (Research Institutions)
There are many data science projects that utilize health data, some of which are run aslarge national-level projects.
The biggest benefit of participating in such projects and conducting data science research is,of course, the ability to access rich and advanced data. However, not all researchers interested
in health data science can participate in such large-scale projects, and depending on the
research topic, there is no necessity to conduct research under such a large project. Due to thesize of the scale, there are many cases where cost-effectiveness deteriorates.
This is why health data science research based on the framework of collaborative healthplus can be executed.
If we can collaborate working with a health insurance society of a large company withthousands to tens of thousands of society members, we can obtain additional informationnecessary for research in the form of questionnaires and measurements by equipment basedon highly accurate and comprehensive health data (homogeneous medical examination data
for all subjects, and medical prescription data for all health care programs except free medicalcare), and conduct research with them together.
By combining existing health insurance data with additional research that is directly
covered, high-quality analytical data of a reasonable size for medical care and health can beobtained at a lower cost, which is the greatest benefit of the collaborative health plus forresearchers.
In addition, it is also beneficial to be able to reduce the percentage of participants who are
falling out when conducting cross-sectional research (panel data research) by data acquisition
over several years. As long as the participant stay with their companies, medical prescription
data can be obtained stably every year, and it is easy to contact them individually to obtain
additional data. If you take a method like a "health examination event" to obtain health data,
it would be difficult to encourage them to attend the event every year for five years, but, withcollaborative health plus, you can expect to get multi-year data with minimal drop-out rates
while keeping costs down.
2) Benefits for Employers and Health Insurance Societies
As a collaborative health business utilizing the health data of the health insurance society,the meaning of various measurements in the medical examination is explained in an
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easy-to-understand manner, and medical examination data such as displaying the change ofthe measured values by comparison with the past figures is utilized, and medical information
is widely provided to employees in the form of indicating changes in medical consultation andmedication based on prescription data.
However, these are only reconstructed medical data and medical data that have alreadybeen acquired, and it is not easy to create more added value than the effect of raising healthawareness through easy-to-understand information provision. In addition, such information
provision business is often offered as a packaged solution by outsourcers, and tends to be a
mediocre output that is not unique.
On the other hand, in the case of collaborative health plus, which works with external
research institutions to acquire and implement new data in addition to the data managed by
the health insurance society, the expertise of external research institutions and additional
data obtained independently may provide significant value-added deliverables not only for
employees but also for employers and the health insurance society.
In addition, by working together with external research institutions in which employers,
health insurance societies, and employees collaborate on research results that are of social
significance, employees will have higher awareness of health and interest in health insurance
and greatly increase the sense of belonging to their companies.
Notes on collaborative health plus implementation
As described, the framework of collaborative health plus has many benefits for both researchersand employers/ health insurance societies, but there are some things to keep in mind when
implementing it.
1)

Additional data to be retrieved is relatively small.
To maximize the benefits of low-cost implementation costs in collaborative health plus, a
research to obtain new data should be relatively small and cost-effective. If the percentage ofadditional surveys increases and the cost of that part increases, the cost reduction benefits ofusing health insurance data at low cost will be reduced, and as a result, it may be more
effective to construct an optimized survey from zero and newly retrieve all the necessary data.

2)

There is a bias in the population of health insurance data.
What you get from the health insurance database is the data of the society members who
belong to the health insurance society. This means that people in their healthy 20s and 50s,
who work for a particular company and their families are targeted. Of course, it does notinclude young people, the elderly, self-employed people, etc., and it is assumed that thecomposition ratio of each job category, the age composition, and the ratio of men and women
will differ greatly depending on the characteristics of the companies associated. For medicalprescription data, information on "non-working layers" may be available to some extent bylooking at dependent data, but it is still not complete, resulting in a clear bias that is limited
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to "office worker's family". In addition, in principle, medical examination data does not include
that of dependents.
When conducting a research using the collaborative health plus framework, you should beaware of these population biases in advance.
However, it is thought that there are a lot of cases that work positively about issues such abias of the target person. This is especially useful when the subject of research is "worker
health and medical care." In such research, for example, if you take a new research design
such as acquiring new health data by holding a "health examination event" during thedaytime on weekdays, only elderly people, housewives, students, etc. who do not work during
the day participate in the event. Since there is a possibility that the data of the working classthat you want to obtain originally cannot be obtained enough, it will be positively meaningful
to utilize the data of the corporate health insurance.
As mentioned earlier in the "Benefits" section, collaborative health plus is a scheme forcross-sectional panel data analysis, so it is desirable to conduct research that tracks aparticular population over the medium to long term and sees relative changes, rather than astudy that takes a snapshot of the overall picture of the health of the people.
3)

The premise is that a scheme to obtain consent separately from the society members is
assumed.
In the collaborative health plus initiative, the medical prescription data of the members
belonging to the health insurance society will be used for the purpose of analyzing therelationship between the newly acquired data by external research institutions, and theresults will also be used by employers.
In many cases, it is not always obvious that such usage is included in the scope ofthird-party use of health insurance data as stipulated in the privacy policy published by eachhealth insurance society. Even if the research is appropriate as a health care project of the
Health Insurance Society, it is safer to assume that it is a prerequisite to obtain individual
consent from the society members when providing health insurance data.
Fortunately, it's not difficult to incorporate the procedure of obtaining consent to provide
health insurance data at the time of retrieving additional data, because the process ofacquiring new data is always included when implementing collaborative health plus.

4)

There is a risk that sufficient number of samples may not be obtained depending on theparticipation rate of additional surveys
Collaborative health plus assumes that you will not only use health insurance data as it is,but will obtain some additional data, such as surveys, data measured with equipment,
participation in exercise and dietary programs, and consent to provide health insurance dataat that time.
Therefore, if there are few employees participating in questionnaires, and events to obtain
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additional data are limited, there is a possibility that the expected number of samples will not
be obtained and the investigation and analysis will not work.
In order to avoid this situation, it is important to communicate with society members inadvance and to keep them updated. It is necessary to show employees the attitude to engage
the top management and managers of the employer and to work together as a whole for thecollaborative health plus.
Adjustment of Interests and the Roles of Employers
The difference between collaborative health plus and the conventional collaborative health isthat it is a scheme in which external research institutions participate in joint research with theaim of providing academic research results.
In the case where external research institutions participate in conventional collaborative health,
it is common for external research institutions to be considered subcontractors from the healthinsurance society. In such a scheme, external organizations are entrusted to carry out theinvestigation and analysis intended by the health insurance society, so there is no deviation of theresearch purpose with it.
However, in the collaborative health plus scheme, external research institutions are not justsubcontractors, but are joint research parties who participate for an independent purpose. For thisreason, "what external research institutions want to do" is not necessarily the same as the“expected results of companies and health insurance societies”, and it is necessary to coordinate
the objectives and interests of both parties.
For example, the rejection of a hypothesis without valid results is common in research to gain
new insights in academic areas. However, the fact that the original hypothesis is rejected as aresult of collaborative health plus research will disappoint the expectations of health insurance
societies and companies that expect concrete results. In addition, it is assumed that the joint
research will be difficult to be successful in case that the research results are hard for companies
and health insurance societies to understand or the research results are difficult to leverage
because the research themes are so niche.
On the other hand, in a situation where the research results expected by companies and healthinsurance societies remain within the range of too common sense and obsolescence, and no further
adventure is allowed, new knowledge cannot be obtained for external research institutions. It isalso assumed that the meaning of conducting joint research will be lost.
In realizing the collaborative health plus initiative, "employers" in particular is expected to playa role in the scheme of three-party collaboration to resolve such misperceptions and conflicts ofinterest, and to coordinate to achieve beneficial results for each party.
Resources from company participating in joint research as "employer" play a role in "connecting"external research institutions and health insurance societies”. One of the greatest roles of"employer" is to discuss and materialize the research design and scope of joint research with11

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external research institutions, taking into account the purpose, motivation, expected results, andinterests of both parties in the joint research.
Joint research at collaborative health plus should include both novel and academicallymeaningful research (even if the hypothesis may be rejected) and research that can reliably obtain
results even if there is not so much novelty, and that the results lead to useful information
provision to health insurance society members and companies participating in the research. Of
course, it would be more desirable if the former research could be beneficial to the healthinsurance and its members, and if the latter research could be designed to be valuable to external
research institutions from the viewpoint of data collection and other sources.

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Part 2 WLAQ Research Report as Collaborative Health “Plus”
Overview of this WLAQ survey
As mentioned earlier, AIG Research Institute, AIG Health Insurance, and JNIOSH haveconcluded the joint research agreement on employee health promotion, and this WLAQ survey ispart of this process.
AIG health insurance society members responded to the WLAQ questionnaire on their lifestyles
and the "oversitting risk" developed by JNIOSH (a hypothesis that continuing the sitting posture
for a long time is a risk factor for lifestyle-related diseases and heart diseases). And AIG ResearchInstitute provided JNIOSH with the collected responses to WLAQ, and AIG Health Insurance
provided the medical prescription data of the respondents to JNIOSH, respectively. Then, JNIOSH
combined the two to conduct a multifaceted analysis of the relationship between oversitting andhealth, which is a main stream of the survey.
In addition, this joint research is a multi-year medium-term project, and WLAQ surveys andanalysis of medical examination and medical prescription data will continue to be conducted in the
future and be expanded to panel data analysis.
This joint research was conducted on oversitting risks and actualized based on the aligned needsof the three parties: JNIOSH, which wants to conduct a medium-term survey and analysis of therelationship between risks and health indicators, and AIG Research Institute, which considers
employee health as "an important risk that should be reduced before problems become apparent"
and makes recommendations for corporate health management through various health promotion
initiatives, and AIG Health Insurance, aiming to further utilize health data and promotecollaborative health as part of its data health plan.

AIG
HealthInsurance
Societymembers

WLAQ survey
execution

Provision of medical
exam./prescription data
Discussion/collaboration on
dataresearchsharing
Joint

JNIOSHagreement(among threeparties)

AIG
InstuituteData
integration/
analysis

Provision of WLAQ
response data

Report/deliverable

Reference: Basic framework for WLAQ research

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About the Joint Research Agreement
The first challenge faced when starting the joint research that is the premise of this entire
research was that it was necessary to initiate a joint research agreement from scratch because theparties’ relationships were more complex than general joint research and outsourcing.
In this joint research, JNIOSH and AIG Research Institute are the main bodies for research andanalysis, and the data handled included medical prescription data managed by AIG Health
Insurance. For this reason, the Joint Research Agreement took a somewhat special form: athree-party agreement between JNIOSH, AIG Research Institute, and AIG Health Insurance.
However, although it is special, it is also considered to be a common problem that often occurs
when conducting joint research in the framework of collaborative health plus.
Of course, even if you cooperate with external research institutions, if you can handle all theprocess management, construction of framework of the company (employee side), planning of joint
research content, and support for the implementation of various negotiations and additional
investigations, etc., it is theoretically possible to conduct joint research (agreements) between
research institutes and health insurance societies, but, it is rare that there are actually enoughresources within the health insurance society to have such planning and management functions.
These roles can be owned by expert staff such as the Human Resources and R&D departments ofthe companies to which the health insurance society belongs to solve such an issue, but in return,the parties to the joint research become three or more parties, and the content of the agreement(contract) becomes complicated.
Since many companies and research institutions only provide two-party contracts as templates
for outsourcing or joint research, it is necessary to significantly rewrite the templates in order toprepare such a joint agreement among three parties. With regard to the joint agreement for the
research, although the existing template is referred to for the structure and articles, the actual
contents of the agreement was transcribed and reviewed repeatedly by the internal legal
department before its completion.
In order to simplify the joint research agreement, specific research plans were split out andagreed in a separate document. In addition, in order to ensure security and anonymization at thetime of data provision, we have also concluded separate service level agreements (SLAs).
Decisions about obtaining consent
As already said, it is desirable to obtain individual consents from society members for providing
medical examination/prescription data.
AIG Health Insurance acquires and manages medical examination data and medicalprescription data for society members, and the usage scope and content of this data are
determined by the privacy policy published on the Health Insurance Website. Although the use ofhealth insurance data in this joint research may be applicable to the purpose of usage, "forpurposes that seem to be beneficial to members, such as the maintenance and promotion of thehealth of members" stated in the applicable privacy policy, considering the characteristics of data
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to be handled and the new initiative to integrate with the result of the questionnaire additionallyacquired, we decided to obtain individual consents.
Flow of WLAQ Questionnaire
In this WLAQ survey, we used a survey tool that is provided as a service on the company's
intranet. The questions were supervised by JNIOSH, and AIG Research Institute executed themon an intranet, conducted surveys, and collected responses.
As for the execution of the survey, we notified the targeted respondents in the form of a mailinglist (managed by the health insurance society) for health insurance society members and news for
employees on the intranet, and the URL link of the questionnaire page was included in the
notification.
Clicking on that URL will take you to the top page of the WLAQ survey, and when you access thesurvey at the top page, you will first be taken to the consent form screen page.

(Reference) Consent form screen image
The consent form screen clearly indicates the overview of this survey, the purpose, the party
executing the survey, and the individual item names of health insurance data (health examination
data, medical prescription data) obtained in conjunction with the questionnaire in whitelist form
(non-comprehensively). Also, it states the data is managed only with anonymous IDs and no
personal information is provided.
Respondents are allowed to withdraw their consents at any time, even after their agreement,
and the contact information when they wish to cancel they participation. In advance, the form of"Consent Withdrawal" was also prepared separately by the secretariat.
At the bottom of the consent form screen, you will see a check box labeled as "I agree to provide
the data”. The "Next" button is activated only if the check box is turned on to the actual WLAQ
questionnaire. If respondents do not agree, they will drop out on this screen.
Respondents who agree to provide data will respond to the WLAQ questionnaire and enter theirhealth insurance certificate numbers. No other personal information will be entered.
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Data anonymization and data provision flow
The response data obtained by the WLAQ survey is individually converted based on theinsurance card numbers using a certain logic, and the anonymized unique IDs (hereinafterreferred to as "IDs") are assigned by AIG Research Institute. Because only AIG Research Instituteknows this ID generation logic, and JNIOSH and AIG Health Insurance do not know it, theconversion from IDs to insurance number certificate numbers cannot be performed by either
JNIOSH or AIG Health Insurance.
Then, AIG Research Institute will send to JNIOSH the WLAQ response data with the insurance
certificate numbers removed and the added IDs, and AIG Research Institute will send to thehealth insurance society the insurance card numbers of the WLAQ respondents (those who agree
on data provision) and the corresponding IDs (the contents of the WLAQ questionnaire notincluded).
The health insurance society extracts medical examination data and medical prescription data
(only items agreed upon in advance) based on the insurance certificate numbers sent by AIG
Research Institute, and then deletes the insurance certificate numbers and adds the IDS before
sending medical examination and medical prescription data to JNIOSH.
JNIOSHdeduplicates

the

names

in

the

WLAQ

questionnaire

andmedicalexamination/prescription data sent by AIG Institutes using the IDs to obtain anonymous data
(analysis master) that combines the responses to the WLAQ questionnaires and health insurance
data. Based on this analysis master, we analyze the relationship between lifestyle and health,including oversitting, and generate analysis reports as the final deliverable.
Medical exam./prescription datawith IDAIG Health
InsuranceJNIOSH

Joint research
agreement(among three parties)

AIG
Institute
Data ofinsurance
certificate
numbers
with IDs of
those who
consented

WLAQ
response data
Assignment of
IDs of those
who
consented

Anonymized
WLAQ
response
data with
IDs of thos
who
consented

Sending anonymized
WLAQ responses with IDs

Sending IDs corresponding to
the insurance certificate
numbers of those who consented

Societymembers

Sending anonymized medical exam./prescription data with ID
Combinedwith IDs
WLAQ＋Medical exam.
＋Medical prescription
data (analysis master)AnalysisAnalysis reportabout
relationshipbetweenoversitting/lifest
yle and health(researchdeliverable)(Reference) WLAQ Survey Data Acquisition, Data Provision and Anonymization Flow
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It seems that this is a complex flow, but actually there are only two important aspects of data
security:

JNIOSH does not receive personal information including insurance certificate numbers.
The name deduplication in data will be executed using IDs, but the insurance certificate
numbers cannot be decrypted based on the IDs.



AIG Research Institute will not receive health insurance data.

Internal communication and response rate
In this WLAQ survey, we took a multi-layered approach as internal communication and worked
to raise awareness and motivate our employees.
Specifically, we conducted the following communications for the survey.
1) News release of the joint research agreement with JNIOSH was also introduced on thecompany intranet.
2) About a week before the survey was conducted, an e-mail was sent to the respondents as a
notice of the survey.
3) An e-mail was sent to the respondents on the start date of the survey.
4)

On the same day, a news article of the start of the investigation was distributed on thecompany intranet.

5) On the day after the survey started, in the blog of the leader in the company, an article waspublished to explain the significance of the research and recommend participation.

Reference: CEO Blog (left) to encourage participation in the WLAQ survey and the Email (right) tohealth insurance society members
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This survey did not provide any special incentives for respondents. In the e-mails and theintranet articles that announce the start of the survey, we strongly encouraged participation by
explaining them the social significance of participation in the survey. Also, we appealed that
response to the survey would increase awareness of their own health.
The above-mentioned internal communication was successful, and the response rate of thissurvey was higher than previously expected.
The WLAQ survey targeted employees who have access to AIG's survey tool (internal intranet)
and are members of AIG Health Insurance, and they are generally consistent with those who arelisted in the "internal email address list of the society members that AIG Health Insurance
knows."
Based on this fact, the response rate was calculated as follows, assuming that the population of
the respondents for the survey can be used as the number of the addresses in the mail address list.
The rate of those who opened the questionnaire page: 25.0% (a)The dropout rate: 1.2% (b)The response rate: 23.8% (c)=(a)-(b)The obvious mistake rate in response: 0.2% (d)The valid response rate: 23.6% (e)=(c)-(d)The percentage of employees who opened the survey start page (the page where the agreement
appears) reached 25.0% or 1/4 of the population. This is a very high percentage from any surveythat does not offer monetary incentive.
Then, the "valid response rate" was 23.6%, excluding cases where the consent form was not
accepted or the questionnaire was not fully answered (1.2%) obvious mistakes were made such asincorrect insurance certificate numbers entered regardless of the fully answered questionnaire(0.2%).
Approximately 2,100 respondents’ data, that is 23.6%, was considered valid responses for thisanalysis.
Reviews, Future Prospects, and Challenges
In addition to support from the company, including the top management, internal humanresources, and public relations departments, due to the close cooperation between the threeparties involved in the joint research, we were able to participate in this WLAQ survey with alarge number of employees, which was a great success. Before the survey, we targeted from 500 to
1,000 valid responses, but in reality we were able to achieve more than twice that and to obtain
excellent data in quantity and quality that could be analyzed with high accuracy.
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This survey does not end in a single year and continues for several years, aiming to further
enhance added value through follow-up surveys (panel data analysis). Based on the success thistime, we will aim to obtain more participants in the next fiscal year and beyond. The results of thissurvey have been fed back to participants as a brief bulletin about two months after execution, andby providing information when the paper is released by JNIOSH, we will also encourage them tounderstand the significance of participating in the survey and continue to participate in the next
surveys.
On the other hand, one of the challenges identified in the data aggregation process is the gap inthe synchronization between survey data and health insurance data. The lifestyles provided as
answers in the WLAQ questionnaire were valid as of the survey conducted (June, 2019), whereas
the health insurance data associated with the answers was valid as of last year.
In particular, since the medical examination data is updated on a yearly basis, if you try toobtain complete data for all employees in the middle of the year, you will inevitably have to use thelast year's data.
Since medical prescription data can be aggregated monthly, it is possible to obtain the mostrecent data, but then, it is not possible to synchronize the medical examination and the medicalprescription data.
Of course, if you wait until the health examination data for the year you conducted the survey
turns available, you can solve this synchronization problem, but then you will have anotherproblem that the timing of analysis will be much later.
However, for this problem, even though consideration is required in the case of a single-year
survey, but if the data of multiple years is accumulated by continuing the survey, the latest data
can be obtained while the data of the past is referred to whenever needed, which will solve theissue over time.
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