"Covid-19 Data in the US Is an ‘Information Catastrophe’" Topic

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Behind the crisis lies a difficult reality: Covid-19 data in the US—in fact, almost all public health data—is chaotic: not one pipe, but a tangle. If the nation had a single, seamless system for collecting, storing, and analyzing health data, HHS and the Coronavirus Task Force would have had a much harder time prying the CDC's Covid-19 data loose. Not having a comprehensive system made the HHS move possible, and however well or badly the department handles the data it will now receive, the lack of a comprehensive data system is harming the US coronavirus response.

"Every health system, every public health department, every jurisdiction really has their own ways of going about things," says Caitlin Rivers, a senior scholar at the Johns Hopkins Center for Health Security. "It's very difficult to get an accurate and timely and geographically resolved picture of what's happening in the US, because there's such a jumble of data."

Data systems are wonky objects, so it may help to step back and explain a little history. First, there's a reason why hospitalization data is important: Knowing whether the demand for beds is rising or falling can help illuminate how hard-hit any area is, and whether reopening in that region is safe.

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California officials whose COVID-19 responses were once hailed as enlightened are now receiving criticism—and some of the sharpest is coming from scientists seeking to help guide the state's fight against the virus. Since April, epidemiologists from Stanford University and several University of California campuses have sought detailed COVID-19 case and contact-tracing data from state and county health authorities for research they hope will point to more effective approaches to slowing the pandemic. "It's a basic mantra of epidemiology and public health: Follow the data" to learn where and how the disease spreads, says Rajiv Bhatia, a physician and epidemiologist who teaches at Stanford and is among those seeking the California data.

But the agencies have refused requests filed from April through late June, Science has learned. They cited multiple reasons including workload constraints and privacy concerns—even though records can be deidentified, and federal health privacy rules have been relaxed for research during the pandemic. As a result, Bhatia says, "In 4 months of the epidemic, collecting millions of records, no one in California or at the CDC [U.S. Centers for Disease Control and Prevention] has done the basic epidemiology." Other states also fail to share highly specific information for their COVID-19 cases, which some scientists warn is hampering efforts to identify targeted measures that could stem the spread of SARS-CoV-2 without full-scale lockdowns.

Follow the money getting paid for treating Covid: link

It's interesting different people pushing whatever agenda, opinion or theory on what the figures for cases, deaths, etc are but you might as well be discussing how many angels can dance on the head of a pin. It's evident that the entire system was unprepared for this and there is not any one person or group to blame. I mean we can't even count votes in a small local election.

Wolfhag

I was wondering the constraint (if any)
HIPAA places on tracing thus research.

A voice of reason. Thank you.

It's no different than any other large scale data integration effort – crime, climate, gun violence, hunger, poverty, etc. Centralization has its own drawbacks, too – loss of control, segment oppression, venue shopping, etc.

but you might as well be discussing how many angels can dance on the head of a pin

I disagree. If you want to make sense, you have to invest significant time into integrating the data, and accept that there are some things you can't find in some data sets (the ambiguity is too high) – just because you have a data type that could answer a question doesn't mean the dataset you have today does answer that question. This is a hard thing for some people to internalize.

I was wondering the constraint (if any) HIPAA places on tracing thus research.

You have to anonymize the data. Since you have to do this at the source, once you combine the data, certain correlations are not possible. F'r'ex if I live in the Democratic People's Republic of Maryland and my son is stationed in the Commonwealth of Virginia, anonymizing our data prevents correlating any potential causal factors across the two incoming datasets. If you link to other data before anonymizing, then you risk double (and triple, and so on) counting effects.

I mean we can't even count votes in a small local election.

Actually, we do that quite well and wouldn't want to subject it to the problems of centralization. Where we have problems is when we believe we can correlate other data (say, demographics of an area) with effects of anonymized data (like election outcomes).

So I can present all the outward behaviours of preferring Candidate X, loudly and frequently. But when I go into the voting booth, I can vote how I want. Then when I come out again, I can verbally commiserate with my dingbat relatives and inbred in-laws about what a shame it is that Candidate Y won.