Japan’s national standardized health checkup program: impacts on self-employed and unemployed populations

Researchers assess how expanding municipal expenses on health checkup programs affect health outcomes and behaviors of self-employed and unemployed citizens

Waseda University

FacebookXLinkedInWeChatBlueskyMessageWhatsAppEmail

 

image: 

A new study has now evaluated the positive impacts of Specific Health Checkups and Specific Health Guidance standardization particularly among the self-employed and unemployed populations. The study highlights the improvements in health outcomes and lifestyle behaviors among these populations, and also the economic viability of expanding municipal resources for scaling up such health checkup programs.

view more 

Credit: Dr. Masato Oikawa from Waseda University, Japan

Rapidly aging populations and rising cases of lifestyle-related diseases (LRDs), like diabetes and hypertension, are driving significant financial strain on government budgets. While regular health checkups under a standardized government program can be a solution, it is not well understood how these initiatives benefit different socioeconomic sections of the society and their economic feasibility. Most studies have documented how health checkup programs affect salaried or employed workers, examining the program’s role in informing individuals about their health status and risks. However, there is limited research on self-employed and unemployed populations, even though these socioeconomic communities are at a greater risk of developing LRDs.

To mitigate this research gap, Assistant Professor Masato Oikawa from the Faculty of Education and Integrated Arts and Sciences, Waseda University, Japan, evaluated the effects of health screening programs on health outcomes, municipal healthcare expenditure, and behaviors among self-employed and unemployed individuals. Dr. Oikawa along with Mr. Takamasa Otake from University of Pennsylvania, United States, Dr. Toshihide Awatani from Kochi Medical School, Japan, Dr. Haruko Noguchi and Dr. Akira Kawamura from Waseda University, used the Specific Health Checkups and Specific Health Guidance (SHC-SHG), implemented by the Japanese government in 2008, as the basis of their assessment.

“We used dosing difference-in-differences estimation and subgroup analysis to explore the effects of increasing municipal expenditure under the SHC-SHG policy standardization on health outcomes and behaviors among the self-employed and unemployed individuals. Additionally, we did a back-of-the-envelope calculation to estimate the cost-effectiveness of this increase in municipal funds for health checkup programs,” explains Dr. Oikawa. Their study was  made available online on August 08, 2025, and was published in Volume 103 of the Journal of Health Economics on September 01, 2025.

The assessments revealed that the SHC-SHG program led to a 16% reduction in the number of self-employed and unemployed people diagnosed with LRDs, with more noticeable effects on individuals with multiple diagnoses than those with a single diagnosis. This suggests that the program is effective in reducing the number of patients with severe conditions, in addition to lowering the overall prevalence of LRD cases.

However, the resultant health improvements were only seen among self-employed individuals and homeowners, but not among unemployed people and renters. Despite this, there were significant good behavioral changes among both self-employed and unemployed people, including a 50% increase in smoking cessation rates, 91% reduction in alcohol consumption, and 163.5% increase in people walking more than 8,000 steps daily.

Furthermore, the increase in municipal health checkup funds was found to be cost-effective, with the overall reduction in medical expenses outweighing the increase in municipal costs by approximately nine times (USD 216.4 million reduction versus USD 23.7 million increase). This suggests that preventive health services and checkups are crucial for improving public health outcomes for lower socioeconomic populations— who face higher risks of LRDs— and thereby mitigating the future financial burden on municipalities.

These findings have clear and immediate applications for policymakers in Japan and other countries facing similar demographic and public health challenges. Policymakers should design and prioritize preventive healthcare programs, particularly for socioeconomically vulnerable populations, to improve public health and reduce long-term costs. Moreover, these programs must be tailored to different socioeconomic groups, addressing the barriers specific to unemployed individuals, such as the perceived high cost of checkups or a lack of understanding about the asymptomatic nature of LRDs.

“Our research demonstrates the economic value of standardized health checkups for improving a country’s overall resilience to public health crises and also sheds light on areas where such programs can be upgraded and made more equitable for everyone. We believe that addressing the health needs of all socioeconomic populations is crucial for the sustainability of the social security system,” concludes Dr. Oikawa.

 

***

Reference
Authors: Masato Oikawaa,b , Takamasa Otakec,b, Toshihide Awatanid,b, Haruko Noguchie,b, and Akira Kawamuraf,b
DOI: 10.1016/j.jhealeco.2025.103046
Affiliations: aFaculty of Education and Integrated Arts and Sciences, Waseda University, Japan
bWaseda Institute of Social & Human Capital Studies (WISH), Japan
cWharton School, University of Pennsylvania, United States
dDepartment of Family Medicine, Kochi Medical School, Japan
eFaculty of Political Science and Economics, Waseda University, Japan
fFaculty of Human Sciences, Waseda University, Japan

About Waseda University
Located in the heart of Tokyo, Waseda University is a leading private research university that has long been dedicated to academic excellence, innovative research, and civic engagement at both the local and global levels since 1882. The University has produced many changemakers in its history, including eight prime ministers and many leaders in business, science and technology, literature, sports, and film. Waseda has strong collaborations with overseas research institutions and is committed to advancing cutting-edge research and developing leaders who can contribute to the resolution of complex, global social issues. The University has set a target of achieving a zero-carbon campus by 2032, in line with the Sustainable Development Goals (SDGs) adopted by the United Nations in 2015. 
To learn more about Waseda University, visit https://www.waseda.jp/top/en

About Assistant Professor Masato Oikawa from Waseda University
Dr. Masato Oikawa has been an Assistant Professor (tenure-track) at the Faculty of Education and Integrated Arts and Sciences, Waseda University, Japan, since April 2025. Before this, he taught as an Assistant Professor in the non-tenure track at other faculties at Waseda University. He completed his Ph.D. in Economics from the University of Tokyo. Dr. Oikawa’s research focus lies in health economics, applied microeconometrics, and economics of education. He has 10 peer-reviewed publications to his name. He is the recipient of the Excellent Paper Award at the 12th Applied Econometrics Conference and the 30th Hongyo Prize from Yokohama National University.

---

Journal

Journal of Health Economics

DOI

10.1016/j.jhealeco.2025.103046 

Method of Research

Data/statistical analysis

Subject of Research

People

Article Title

Impacts of health checkup programs standardization on working-age self-employed and unemployed: Insights from Japan’s local government response to national policy

Article Publication Date

30-Sep-2025

 

How could AI help (and hurt) forestry?

Northern Arizona University

BlueskyMessageWhatsAppEmail

The whole world is buzzing about the potential and pitfalls of artificial intelligence—including those who work in forestry.

AI could revolutionize forestry, making it possible to save more lives and ecosystems through faster and more accurate data analysis. But if forestry professionals aren’t careful, AI could also botch critical land-management and policy decisions. 

That’s why NAU School of Forestry faculty members Alark Saxena, Luke Ritter and Derek Uhey took it upon themselves to understand foresters’ relationship with AI: how they’re using it now, how they hope to leverage it in the future and what concerns them. They conducted 20 in-depth interviews with forestry professionals in the Southwest and recently published their findings in Forest Policy and Economics. 

“We noticed a great deal of discussion about the potential of AI in forestry, but very little research on how the professionals on the ground actually feel about it,” Saxena, an associate professor of human dimensions of forestry, said. “This study was our first investigation into the topic, motivated by the need to understand the human side of this technological shift.” 

In their interviews with foresters working across academia, government and private industry, the research team discovered that no one in forestry wants AI to replace human expertise or make critical decisions without oversight from real people.

“They are particularly concerned about the ‘black box’ problem where they can’t understand AI’s decision-making process, creating serious accountability issues,” Saxena said. “A key concern they shared was the risk of training AI using some agencies’ poor-quality or biased data and then trusting its flawed outputs for important land management or policy analysis”—like mandates on where to administer prescribed burns or allow clear-cutting.

But the forestry professionals they interviewed agreed AI could be a useful tool in supporting some aspects of their work. With current labor shortages leaving them overworked and burned out, the workers agreed they’d welcome AI help with monotonous tasks like summarizing information, lesson planning and filling out routine paperwork.

“They also see great potential in using AI for complex data analysis, such as with light detection and ranging, as long as it functions as an assistant that enhances, rather than replaces, the judgment of an experienced professional,” Saxena said.

Ritter said he hopes others across the United States and the globe will conduct interviews with forestry professionals to capture a wider range of perspectives on AI. Getting a full understanding of professionals’ fears about—and recommendations for—the use of AI could help leaders create policies that guide future forestry work.

"It's challenging to ethically implement AI when we, as foresters, have gaps in our knowledge about how and why it's being used,” Ritter said. “This study highlighted some interesting themes, but we need to keep discussing AI in the classroom and the workplace. We hope this paper provides a foundation for policy changes and further research as AI continues to grow."

---

Journal

Forest Policy and Economics

DOI

10.1016/j.forpol.2025.103626 

Subject of Research

Not applicable

Article Title

Knowledge, attitudes, and practices of forestry professionals towards artificial intelligence (AI)☆

Article Publication Date

1-Oct-2025

 

AI system learns from many types of scientific information and runs experiments to discover new materials

The new “CRESt” platform could help find solutions to real-world energy problems that have plagued the materials science and engineering community for decades.

Massachusetts Institute of Technology

Facebook

XLinkedInWeChatBlueskyMessageWhatsAppEmail

Machine-learning models can speed up the discovery of new materials by making predictions and suggesting experiments. But most models today only consider a few specific types of data or variables. Compare that with human scientists, who work in a collaborative environment and consider experimental results, the broader scientific literature, imaging and structural analysis, personal experience or intuition, and input from colleagues and peer reviewers.

Now, MIT researchers have developed a method for optimizing materials recipes and planning experiments that incorporates information from diverse sources like insights from the literature, chemical compositions, microstructural images, and more. The approach is part of a new platform, named Copilot for Real-world Experimental Scientists (CRESt), that also uses robotic equipment for high-throughput materials testing, the results of which are fed back into large multimodal models to further optimize materials recipes.

Human researchers can converse with the system in natural language, with no coding required, and the system makes its own observations and hypotheses along the way. Cameras and visual language models also allow the system to monitor experiments, detect issues, and suggest corrections.

“In the field of AI for science, the key is designing new experiments,” says Ju Li, School of Engineering Carl Richard Soderberg Professor of Power Engineering. “We use multimodal feedback — for example information from previous literature on how palladium behaved in fuel cells at this temperature, and human feedback — to complement experimental data and design new experiments. We also use robots to synthesize and characterize the material’s structure and to test performance.”

The system is described in a paper published in Nature. The researchers used CRESt to explore more than 900 chemistries and conduct 3,500 electrochemical tests, leading to the discovery of a catalyst material that delivered record power density in a fuel cell that runs on formate salt to produce electricity.

Joining Li on the paper as first authors are PhD student Zhen Zhang, Zhichu Ren PhD ’24, PhD student Chia-Wei Hsu, and postdoc Weibin Chen. Their coauthors are MIT Assistant Professor Iwnetim Abate; Associate Professor Pulkit Agrawal; JR East Professor of Engineering Yang Shao-Horn; MIT.nano researcher Aubrey Penn; Zhang-Wei Hong PhD ’25, Hongbin Xu PhD ’25; Daniel Zheng PhD ’25; MIT graduate students Shuhan Miao and Hugh Smith; MIT postdocs Yimeng Huang, Weiyin Chen, Yungsheng Tian, Yifan Gao, and Yaoshen Niu; former MIT postdoc Sipei Li; and collaborators including Chi-Feng Lee, Yu-Cheng Shao, Hsiao-Tsu Wang, and Ying-Rui Lu.

A smarter system

Materials science experiments can be time-consuming and expensive. They require researchers to carefully design workflows, make new material, and run a series of tests and analysis to understand what happened. Those results are then used to decide how to improve the material.

To improve the process, some researchers have turned to a machine-learning strategy known as active learning to make efficient use of previous experimental data points and explore or exploit those data. When paired with a statistical technique known as Bayesian optimization (BO), active learning has helped researchers identify new materials for things like batteries and advanced semiconductors.

“Bayesian optimization is like Netflix recommending the next movie to watch based on your viewing history, except instead it recommends the next experiment to do,” Li explains. “But basic Bayesian optimization is too simplistic. It uses a boxed-in design space, so if I say I’m going to use platinum, palladium, and iron, it only changes the ratio of those elements in this small space. But real materials have a lot more dependencies, and BO often gets lost.”

Most active learning approaches also rely on single data streams that don’t capture everything that goes on in an experiment. To equip computational systems with more human-like knowledge, while still taking advantage of the speed and control of automated systems, Li and his collaborators built CRESt. 

CRESt’s robotic equipment includes a liquid-handling robot, a carbothermal shock system to rapidly synthesize materials, an automated electrochemical workstation for testing, characterization equipment including automated electron microscopy and optical microscopy, and auxiliary devices such as pumps and gas valves, which can also be remotely controlled.  Many processing parameters can also be tuned.

With the user interface, researchers can chat with CRESt and tell it to use active learning to find promising materials recipes for different projects. CRESt can include up to 20 precursor molecules and substrates into its recipe. To guide material designs, CRESt’s models search through scientific papers for descriptions of elements or precursor molecules that might be useful. When human researchers tell CRESt to pursue new recipes, it kicks off a robotic symphony of sample preparation, characterization, and testing. The researcher can also ask CRESt to perform image analysis from scanning electron microscopy imaging, X-ray diffraction, and other sources.

Information from those processes is used to train the active learning models, which use both literature knowledge and current experimental results to suggest further experiments and accelerate materials discovery.

“For each recipe we use previous literature text or databases, and it creates these huge representations of every recipe based on the previous knowledge base before even doing the experiment,” says Li. “We perform principal component analysis in this knowledge embedding space to get a reduced search space that captures most of the performance variability. Then we use Bayesian optimization in this reduced space to design the new experiment. After the new experiment, we feed newly acquired multimodal experimental data and human feedback into a large language model to augment the knowledgebase and redefine the reduced search space, which gives us a big boost in active learning efficiency.”

Materials science experiments can also face reproducibility challenges. To address the problem, CRESt monitors its experiments with cameras, looking for potential problems and suggesting solutions via text and voice to human researchers.

The researchers used CRESt to develop an electrode material for an advanced type of high-density fuel cell known as a direct formate fuel cell. After exploring more than 900 chemistries over three months, CRESt discovered a catalyst material made from eight elements that achieved a 9.3-fold improvement in power density per dollar over pure palladium, an expensive precious metal. In further tests, CRESTs material was used to deliver a record power density to a working direct formate fuel cell even though the cell contained just one-fourth of the precious metals of previous devices.

The results show the potential for CRESt to find solutions to real-world energy problems that have plagued the materials science and engineering community for decades.

“A significant challenge for fuel-cell catalysts is the use of precious metal,” says Zhang. “For fuel cells, researchers have used various precious metals like palladium and platinum. We used a multielement catalyst that also incorporates many other cheap elements to create the optimal coordination environment for catalytic activity and resistance to poisoning species such as carbon monoxide and adsorbed hydrogen atom. People have been searching low-cost options for many years. This system greatly accelerated our search for these catalysts.”

A helpful assistant

Early on, poor reproducibility emerged as a major problem that limited the researchers’ ability to perform their new active learning technique on experimental datasets. Material properties can be influenced by the way the precursors are mixed and processed, and any number of problems can subtly alter experimental conditions, requiring careful inspection to correct.

To partially automate the process, the researchers coupled computer vision and vision language models with domain knowledge from the scientific literature, which allowed the system to hypothesize sources of irreproducibility and propose solutions. For example, the models can notice when there’s a millimeter-sized deviation in a sample’s shape or when a pipette moves something out of place. The researchers incorporated some of the model’s suggestions, leading to improved consistency, suggesting the models already make good experimental assistants.

The researchers noted that humans still performed most of the debugging in their experiments.

“CREST is an assistant, not a replacement, for human researchers,” Li says. “Human researchers are still indispensable. In fact, we use natural language so the system can explain what it is doing and present observations and hypotheses. But this is a step toward more flexible, self-driving labs.”

###

Written by Zach Winn, MIT News

---

Journal

Nature

DOI

10.1038/s41586-025-09640-5 

Article Title

"A multimodal robotic platform for multi-element electrocatalyst discovery"

 

UAlbany Atmospheric scientists awarded $855K NOAA grant for water isotope research

University at Albany, SUNY

Facebook

MessageWhatsAppEmail

 

image: 

A small body of water overlooks the mountains in Mendoza, Argentina.

view more 

Credit: Photo courtesy of Laura Gil Martínez / IAEA

ALBANY, N.Y. (Sept. 25, 2025) — Researchers at the University at Albany are exploring a new method to improve weather and climate forecasts that relies on a tiny but powerful assistant — stable water isotopes.

Water isotopes are the naturally occurring variations of hydrogen and oxygen atoms within water molecules. Isotopes have slightly different masses but the same chemical properties, acting like fingerprints that reveal information about a sample’s origin and history.

By measuring differences in isotope masses in rainfall, snow, or even ice, scientists can trace where moisture came from, how it traveled, and the weather conditions it experienced along the way.

Sarah Lu, research faculty at UAlbany’s Atmospheric Sciences Research Center, is leading a three-year, $855,162 project funded by the National Oceanic and Atmospheric Administration (NOAA) to integrate water isotopes into NOAA’s Unified Forecast System.  

The project is supported by a team of researchers from NOAA, UAlbany and Boston College.  

“As natural tracers of moisture exchange, water isotopes provide a unique view into the water cycle across time scales,” said Lu, the project’s principal investigator. “Their tiny mass differences allow scientists to track water movement, including precipitation, and better understand related processes. Our goal is to use these isotopic tracers to study hydrological processes and their uncertainties, which could significantly improve weather predictions.”

The Unified Forecast System (UFS) is an open-source, community-based Earth modeling system, designed as both a research tool and to support weather and climate forecasting. It is designed to unify NOAA's diverse and complex forecasting systems into a single framework.

Over the next three years, Lu’s team will create a tool that integrates existing water isotope measurements into the UFS. The isotope measurements were recently collected in liquid and vapor phases from ground stations, aircraft, ships and satellites.

The research team is developing the new tool to investigate precipitation and other hydrological processes, with a focus on extreme events such as the Madden-Julian Oscillation, a tropical climate pattern that drives rainfall around the globe, atmospheric rivers and the North American monsoon.

Their findings and water isotope datasets will be shared with the broader UFS community.

“This new tool will allow scientists to use the UFS to diagnose and investigate precipitation and hydrological processes,” said Lu. “By adding this capability, we can better study extreme precipitation events and thus improve our weather prediction models.”

Yi Ming, a professor in the Department of Earth and Environmental Sciences at Boston College, is partnering with Lu as the project’s co-principal investigator.

Other UAlbany researchers involved with the project include Scott Miller and Shih-Wei Wei of the Atmospheric Sciences Research Center and Mathias Vuille and Zhiqiang Lyu of the Department of Atmospheric and Environmental Sciences.  

The project will also support a graduate student researcher and two early-career scientists. 

 

Beyond viruses: Expanding the fight against infectious diseases

The newly renamed Gladstone Infectious Disease Institute has broadened its mission to address global health threats ranging from antibiotic resistance to infections that cause chronic diseases.

Gladstone Institutes

Facebook

XLinkedInWeChatBlueskyMessageWhatsAppEmail

 

image: 

Melanie Ott, MD, PhD, is director of the newly renamed Gladstone Infectious Disease Institute, which has broadened its mission to address global health threats ranging from antibiotic resistance to infections that cause chronic diseases. 

view more 

Credit: Gladstone Institutes

From influenza and COVID-19 to HIV, viruses continue to pose a serious danger to global health.

But just as pressing are threats from other disease-causing microorganisms such as bacteria—especially the deadly strains that are becoming resistant to antibiotic medicines. And increasingly, scientists are discovering how viruses and bacteria are closely interconnected, influencing health and disease in ways that we’re only beginning to understand.

To reflect this reality, the Gladstone Institute of Virology has taken on a new name: the Gladstone Infectious Disease Institute. The new name aptly encompasses the expanded nature of the institute’s mission to understand and address a range of infectious threats.

“We’re building on the institute’s deep expertise studying viruses to make new discoveries that can impact a greater number of the pressing health challenges we face,” says Melanie Ott, MD, PhD, director of the Gladstone Infectious Disease Institute and the Nick and Sue Hellmann Distinguished Professor at Gladstone Institutes. “The name change is emblematic of our broadened research scope and sets us on the right track for the next decades.”

The Gladstone Infectious Disease Institute is one of five institutes that make up Gladstone Institutes, a San Francisco–based biomedical research organization dedicated to finding cures for the world’s most devastating diseases, including heart disease, neurodegenerative disorders, and cancer.

“As science continuously evolves, we evolve with it,” says Gladstone President Deepak Srivastava, MD. “The expanded vision for our infectious disease research is a strategic decision that will empower us to tackle the global health challenges that lie ahead.”

Evolving to Meet Critical Research Needs

The Gladstone Institute of Virology was established in 1992, originally as the Gladstone Institute of Virology and Immunology. Over time, the institute made its mark across many disciplines, with HIV as an initial and continuing focus.

By discovering how HIV hijacks our immune cells, Gladstone discoveries helped lay the foundation for drugs that have converted AIDS from a universally fatal disease to a chronic condition. In addition, the institute’s scientists led a global study that resulted in FDA approval of the HIV pre-exposure prophylaxis (PReP) drug Truvada, establishing an efficient way to prevent new infections in high-risk populations—now the standard of care around the world.

They also made significant discoveries that led to a better understanding of long COVID, identified powerful drug candidates that could head off future coronavirus pandemics, and provided novel insights into the function of 70,000 lesser-known viral proteins that could help in the development of new antiviral therapies.

“As the Gladstone Infectious Disease Institute, we remain dedicated to these important areas of virology research,” Ott says. “Not only are we still determined to find a cure for HIV, but we’re leveraging the lessons we’ve learned from studying that complex virus to develop new ways to detect and treat many other types of viral infections.”

While researchers in the institute will continue to study viruses including HIV, SARS-CoV-2 (especially in the context of long COVID), Zika, hepatitis C, and influenza, some will delve into new areas of biology.

One team, for instance, is developing novel approaches to make vaccines more effective, and even applying the knowledge to create therapeutic vaccines that can treat cancer.

When Viruses and Bacteria Collide

Across the globe, bacteria that infect humans are evolving mechanisms to evade the medicines designed to kill them. As antibiotics become less and less effective against many types of bacteria, infections like pneumonia and tuberculosis become harder—or, in some cases, impossible—to treat, and routine medical procedures become much riskier.

Within the Gladstone Infectious Disease Institute, scientists are looking at alternative ways to overcome this antibiotic resistance, particularly through harnessing the therapeutic power of bacteriophages—more commonly known as “phages.” Phages are viruses that naturally infect and often kill bacteria in our bodies, making them a promising alternative for treating infections.

One team is carrying out large-scale screens of tens of thousands of phages to identify those with the best potential to counter today’s top antibacterial threats. Another group developed a technology to edit the genomes of phages as a way of engineering them into efficient bacteria-killing machines.

Gladstone scientists are also developing tools to better diagnose viral infections and bacterial diseases such as tuberculosis. For instance, during the COVID-19 pandemic, a team outlined the technology for a rapid, one-step test to detect SARS-CoV-2 using a smartphone camera.

Delving Into the Microbiome

The institute’s researchers also are studying the human microbiome, the complex community of microorganisms—including bacteria, viruses, fungi, and protozoa—that live in and on the human body. In recent years, conditions ranging from autoimmune diseases to psychiatric conditions have been linked to an imbalance in the body’s microbial communities.

Gladstone scientists have developed computational tools to better predict diseases based on microbiome profiles and showed that even a mild SARS-CoV-2 infection can cause long-lasting instability in the gut microbiome.

“It’s nearly impossible today to study the human virome—or the collection of viruses in and on the human body—without also considering the influence of bacteria,” Ott says. “Bacteria not only cause disease, but they also carry viruses. And together, they influence our health in ways we have not yet fully understood.”

“With our new name, we’re taking on this bigger mission,” she adds. “We look forward to continuing to expand the bounds of scientific knowledge on infectious diseases to make breakthroughs that improve global health.”

About Gladstone Institutes

Gladstone Institutes is an independent, nonprofit life science research organization that uses visionary science and technology to overcome disease. Established in 1979, it is located in the epicenter of biomedical and technological innovation, in the Mission Bay neighborhood of San Francisco. Gladstone has created a research model that disrupts how science is done, funds big ideas, and attracts the brightest minds.

---

Method of Research

News article