Cities, health, and the big data revolution

Curated on Posted on by Stefaan Verhulst

Blog by Harvard Public Health: “Cities influence our health in unexpected ways. From sidewalks to crosswalks, the built environment affects how much we move, impacting our risk for diseases like obesity and diabetes. A recent New York City study underscores that focusing solely on infrastructure, without understanding how people use it, can lead to ineffective interventions. Researchers analyzed over two million Google Street View images, combining them with health and demographic data to reveal these dynamics. Harvard Public Health spoke with Rumi Chunara, director of New York University’s Center for Health Data Science and lead author of the study.

Why study this topic?

We’re seeing an explosion of new data sources, like street-view imagery, being used to make decisions. But there’s often a disconnect—people using these tools don’t always have the public health knowledge to interpret the data correctly. We wanted to highlight the importance of combining data science and domain expertise to ensure interventions are accurate and impactful.

What did you find?

We discovered that the relationship between built environment features and health outcomes isn’t straightforward. It’s not just about having sidewalks; it’s about how often people are using them. Improving physical activity levels in a community could have a far greater impact on health outcomes than simply adding more infrastructure.

It also revealed the importance of understanding the local context. For instance, Google Street View data sometimes misclassifies sidewalks, particularly near highways or bridges, leading to inaccurate conclusions. Relying solely on this data, without accounting for these nuances, could result in less effective interventions…(More)”.

Randomize NIH grant giving

Curated on Posted on by Stefaan Verhulst

Article by Vinay Prasad: “A pause in NIH study sections has been met with fear and anxiety from researchers. At many universities, including mine, professors live on soft money. No grants? If you are assistant professor, you can be asked to pack your desk. If you are a full professor, the university slowly cuts your pay until you see yourself out. Everyone talks about you afterwards, calling you a failed researcher. They laugh, a little too long, and then blink back tears as they wonder if they are next. Of course, your salary doubles in the new job and you are happier, but you are still bitter and gossiped about.

In order to apply for NIH grants, you have to write a lot of bullshit. You write specific aims and methods, collect bios from faculty and more. There is a section where you talk about how great your department and team is— this is the pinnacle of the proverbial expression, ‘to polish a turd.’ You invite people to work on your grant if they have a lot of papers or grants or both, and they agree to be on your grant even though they don’t want to talk to you ever again.

You submit your grant and they hire someone to handle your section. They find three people to review it. Ideally, they pick people who have no idea what you are doing or why it is important, and are not as successful as you, so they can hate read your proposal. If, despite that, they give you a good score, you might be discussed at study section.

The study section assembles scientists to discuss your grant. As kids who were picked last in kindergarten basketball, they focus on the minutiae. They love to nitpick small things. If someone on study section doesn’t like you, they can tank you. In contrast, if someone loves you, they can’t really single handedly fund you.

You might wonder if study section leaders are the best scientists. Rest assured. They aren’t. They are typically mid career, mediocre scientists. (This is not just a joke, data support this claim see www.drvinayprasad.com). They rarely have written extremely influential papers.

Finally, your proposal gets a percentile score. Here is the chance of funding by percentile. You might get a chance to revise your grant if you just fall short….Given that the current system is onerous and likely flawed, you would imagine that NIH leadership has repeatedly tested whether the current method is superior than say a modified lottery, aka having an initial screen and then randomly giving out the money.

Of course not. Self important people giving out someone else’s money rarely study their own processes. If study sections are no better than lottery, that would mean a lot of NIH study section officers would no longer need to work hard from home half the day, freeing up money for one more grant.

Let’s say we take $200 million and randomize it. Half of it is allocated to being given out in the traditional method, and the other half is allocated to a modified lottery. If an application is from a US University and passes a minimum screen, it is enrolled in the lottery.

Then we follow these two arms into the future. We measure publications, citations, h index, the average impact factor of journals in which the papers are published, and more. We even take a subset of the projects and blind reviewers to score the output. Can they tell which came from study section?…(More)”.

Will big data lift the veil of ignorance?

Curated on Posted on by Stefaan Verhulst

Blog by Lisa Herzog: “Imagine that you have a toothache, and a visit at the dentist reveals that a major operation is needed. You phone your health insurance. You listen to the voice of the chatbot, press the buttons to go through the menu. And then you hear: “We have evaluated your profile based on the data you have agreed to share with us. Your dental health behavior scores 6 out of 10. The suggested treatment plan therefore requires a co-payment of [insert some large sum of money here].”

This may sound like science fiction. But many other insurances, e.g. car insurances, already build on automated data being shared with them. If they were allowed, health insurers would certainly like to access our data as well – not only those from smart toothbrushes, but also credit card data, behavioral data (e.g. from step counting apps), or genetic data. If they were allowed to use them, they could move towards segmented insurance plans for specific target groups. As two commentators, on whose research I come back below, recently wrote about health insurance: “Today, public plans and nondiscrimination clauses, not lack of information, are what stands between integration and segmentation.”

If, like me, you’re interested in the relation between knowledge and institutional design, insurance is a fascinating topic. The basic idea of insurance is centuries old – here is a brief summary (skip a few paragraphs if you know this stuff). Because we cannot know what might happen to us in the future, but we can know that on an aggregate level, things will happen to people, it can make sense to enter an insurance contract, creating a pool that a group jointly contributes to. Those for whom the risks in question materialize get support from the pool. Those for whom it does not materialize may go through life without receiving any money, but they still know that they could get support if something happened to them. As such, insurance combines solidarity within a group with individual pre-caution…(More)”.

Thousands of U.S. Government Web Pages Have Been Taken Down Since Friday

Curated on Posted on by Stefaan Verhulst

Article by Ethan Singer: “More than 8,000 web pages across more than a dozen U.S. government websites have been taken down since Friday afternoon, a New York Times analysis has found, as federal agencies rush to heed President Trump’s orders targeting diversity initiatives and “gender ideology.”

The purges have removed information about vaccines, veterans’ care, hate crimes and scientific research, among many other topics. Doctors, researchers and other professionals often rely on such government data and advisories. Some government agencies appear to have removed entire sections of their websites, while others are missing only a handful of pages.

Among the pages that have been taken down:

(The links are to archived versions.)

Developing a theory of robust democracy

Curated on Posted on by Stefaan Verhulst

Paper by Eva Sørensen and Mark E. Warren: “While many democratic theorists recognise the necessity of reforming liberal democracies to keep pace with social change, they rarely consider what enables such reform. In this conceptual article, we suggest that liberal democracies are politically robust when they are able to continuously adapt and innovate how they operate when doing so is necessary to continue to serve key democratic functions. These functions include securing the empowered inclusion of those affected, collective agenda setting and will formation, and the making of joint decisions. Three current challenges highlight the urgency of adapting and innovating liberal democracies to become more politically robust: an increasingly assertive political culture, the digitalisation of political communication and increasing global interdependencies. A democratic theory of political robustness emphasises the need to strengthen the capacity of liberal democracies to adapt and innovate in response to changes, just as it helps to frame the necessary adaptations and innovations in times such as the present…(More)”

Establish data collaboratives to foster meaningful public involvement

Curated on Posted on by Stefaan Verhulst

Article by Gwen Ottinger: “…Data Collaboratives would move public participation and community engagement upstream in the policy process by creating opportunities for community members to contribute their lived experience to the assessment of data and the framing of policy problems. This would in turn foster two-way communication and trusting relationships between government and the public. Data Collaboratives would also help ensure that data and their uses in federal government are equitable, by inviting a broader range of perspectives on how data analysis can promote equity and where relevant data are missing. Finally, Data Collaboratives would be one vehicle for enabling individuals to participate in science, technology, engineering, math, and medicine activities throughout their lives, increasing the quality of American science and the competitiveness of American industry…(More)”.

Experts warn about the ‘crumbling infrastructure’ of federal government data

Curated on Posted on by Stefaan Verhulst

Article by Hansi Lo Wang: “The stability of the federal government’s system for producing statistics, which the U.S. relies on to understand its population and economy, is under threat because of budget concerns, officials and data users warn.

And that’s before any follow-through on the new Trump administration and Republican lawmakers‘ pledges to slash government spending, which could further affect data production.

In recent months, budget shortfalls and the restrictions of short-term funding have led to the end of some datasets by the Bureau of Economic Analysis, known for its tracking of the gross domestic product, and to proposals by the Bureau of Labor Statistics to reduce the number of participants surveyed to produce the monthly jobs report. A “lack of multiyear funding” has also hurt efforts to modernize the software and other technology the BLS needs to put out its data properly, concluded a report by an expert panel tasked with examining multiple botched data releases last year.

Long-term funding questions are also dogging the Census Bureau, which carries out many of the federal government’s surveys and is preparing for the 2030 head count that’s set to be used to redistribute political representation and trillions in public funding across the country. Some census watchers are concerned budget issues may force the bureau to cancel some of its field tests for the upcoming tally, as it did with 2020 census tests for improving the counts in Spanish-speaking communities, rural areas and on Indigenous reservations.

While the statistical agencies have not been named specifically, some advocates are worried that calls to reduce the federal government’s workforce by President Trump and the new Republican-controlled Congress could put the integrity of the country’s data at greater risk…(More)”

Impact Curious?

Curated on Posted on by Stefaan Verhulst

Excerpt of book by Priya Parrish: “My journey to impact investing began when I was an undergraduate studying economics and entrepreneurship and couldn’t find any examples of people harnessing the power of business to improve the world. That was 20 years ago, before impact investing was a recognized strategy. Back then, just about everyone in the field was an entrepreneur experimenting with investment tools, trying to figure out how to do well financially while also making positive change. I joined right in.

The term “impact investing” has been around since 2007 but hasn’t taken hold the way I thought (and hoped) it might. There are still a lot of myths about what impact investing truly is and does, the most prevalent of which is that doing good won’t generate returns. This couldn’t be more false, yet it persists. This book is my attempt to debunk this myth and others like it, as well as make sense of the confusion, as it’s difficult for a newcomer to understand the jargon, sort through the many false or exaggerated claims, and follow the heated debates about this topic. This book is for the “impact curious,” or anyone who wants more than just financial returns from their investments. It is for anyone interested in finding out what their investments can do when aligned with purpose. It is for anyone who wishes to live in alignment with their values—in every aspect of their lives.

This particular excerpt from my book, The Little Book of Impact Investing, provides a history of the term and activity in the space. It addresses why now is a particularly good time to make business do more and do better—so that the world can and will too…(More)”.

Silencing Science Tracker

Curated on Posted on by Stefaan Verhulst

About: “The Silencing Science Tracker is a joint initiative of the Sabin Center for Climate Change Law and the Climate Science Legal Defense Fund. It is intended to record reports of federal, state, and local government attempts to “silence science” since the November 2016 election.

We define “silencing science” to include any action that has the effect of restricting or prohibiting scientific research, education, or discussion, or the publication or use of scientific information. We divide such actions into 7 categories as follows…(More)”

CategoryExamples
Government CensorshipChanging the content of websites and documents to suppress or distort scientific information.Making scientific data more difficult to find or access.Restricting public communication by scientists.
Self-CensorshipScientists voluntarily changing the content of websites and documents to suppress or distort scientific information, potentially in response to political pressure.
 We note that it is often difficult to determine whether self-censorship is occurring and/or its cause. We do not take any position on the accuracy of any individual report on self-censorship.
Budget CutsReducing funding for existing agency programs involving scientific research or scientific education.Cancelling existing grants for scientific research or scientific education.
 We do not include, in the “budget cuts” category, government decisions to refuse new grant applications or funding for new agency programs.
Personnel ChangesRemoving scientists from agency positions or creating a hostile work environment.Appointing unqualified individuals to, or failing to fill, scientific positions.Changing the composition of scientific advisory board or other bodies to remove qualified scientists or add only industry-favored members.Eliminating government bodies involved in scientific research or education or the dissemination of scientific information.
Research HindranceDestroying data needed to undertake scientific research.Preventing or restricting the publication of scientific research.Pressuring scientists to change research findings.
Bias and MisrepresentationEngaging in “cherry picking” or only disclosing certain scientific studies (e.g., that support a particular conclusion).Misrepresenting or mischaracterizing scientific studies.Disregarding scientific studies or advice in policy-making.
Interference with EducationChanging science education standards to prevent or limit the teaching of proven scientific theories.Requiring or encouraging the teaching of discredited or unproven scientific theories.Preventing the use of factually accurate textbooks and other instructional materials (e.g., on religious grounds).

Good government data requires good statistics officials – but how motivated and competent are they?

Curated on Posted on by Stefaan Verhulst

Worldbank Blog: “Government data is only as reliable as the statistics officials who produce it. Yet, surprisingly little is known about these officials themselves. For decades, they have diligently collected data on others –  such as households and firms – to generate official statistics, from poverty rates to inflation figures. Yet, data about statistics officials themselves is missing. How competent are they at analyzing statistical data? How motivated are they to excel in their roles? Do they uphold integrity when producing official statistics, even in the face of opposing career incentives or political pressures? And what can National Statistical Offices (NSOs) do to cultivate a workforce that is competent, motivated, and ethical?

We surveyed 13,300 statistics officials in 14 countries in Latin America and the Caribbean to find out. Five results stand out. For further insights, consult our Inter-American Development Bank (IDB) report, Making National Statistical Offices Work Better.

1. The competence and management of statistics officials shape the quality of statistical data

Our survey included a short exam assessing basic statistical competencies, such as descriptive statistics and probability. Statistical competence correlates with data quality: NSOs with higher exam scores among employees tend to achieve better results in the World Bank’s Statistical Performance Indicators (r = 0.36).

NSOs with better management practices also have better statistical performance. For instance, NSOs with more robust recruitment and selection processes have better statistical performance (r = 0.62)…(More)”.