Bright Simons writes: Is open data the enemy in Ghana’s COVID-19 fight?

On April 11, 2020, I wrote a blogpost warning that if the government of Ghana fails to get its data management under control, it will start to lose public trust, regardless of how well the actual management of the Covid-19 outbreak itself was going.

On April 19,  2020, the President of Ghana announced to Ghanaians that he was, effective April 20, lifting the “partial lockdown” imposed on the country since March 30, 2020.

He cited the “robustness of data” and the “constancy” of the situation as the basis for the decision. In an earlier article, I have discussed why the President’s assurances did not go down well with many of the country’s health leaders.

The government has left the metrics and indicators that will trigger specific actions (such as the loosening of restrictions) much too loose and ad hoc. The trade-off of this flexibility is second-guessing by experts outside the government’s team.

In this shorter article, I shall be listing, for those who could not read any of the earlier posts, the outstanding data-related issues that will continue to fuel controversy if not comprehensively addressed.

1. The GHS’ public bulletins are too data-thin and patchy to serve researchers

The Ghana Health Service (GHS) releases periodic updates on a portal to inform the public about new developments. Compared to the “situational reports” prepared at regional and district levels for health administrators, the data on the portal (though visualization has been improving) is not very useful to researchers as it lacks granularity. None of the reports circulating around is in a format that can be exported to spreadsheets for analysis anyway. The inability of independent researchers to build models to explain features of the disease phenomena is forcing them to speculate and rely on the grapevine. Experts without the help of data are rarely more insightful than ordinary joes.

2. Beyond data on the spread of the disease, there is also no information on the protocols guiding the response itself

The COVID-19 response in Ghana is, according to the authorities, based on three main pillars: trace, test and treat (including isolation if necessary). Each of these strategies has many underpinning operational elements. And none of them are being guided by published, widely available, national protocols and standard operating procedures. In the absence of documentation, speculations are rife about all manner of things. In this short post, we will deal primarily with the first two strategies: trace and test.

3. The “supply chain” for delivering tests to people needs work

The country’s mass testing protocol has many gaps and lags. Clinical referrals for testing during routine surveillance (where people with COVID-19-like symptoms are identified by clinicians and sampled at a health facility) are currently not automated. The performance of the different tracing teams (public health personnel who actively search for cases in the community) differs considerably. Samples need aggregation before they are sent off to the various labs (virtually all of them in the country’s two major cities of Accra and Kumasi), but the inefficiencies can affect the quality of the sample and thus testing integrity.

Per WHO and US CDC standards, samples need a cold chain at all times. If a sample will take more than 5 days before reaching the lab, dry ice (-70 degrees celsius) is required. Ideally, samples should be transported in protein-antibiotic complexes called viral transport media (VTM). Some laboratory scientists complain of some delivered samples lacking even basic saline buffers, arriving unsealed, or having such small sample volumes as to interfere with viral RNA extraction.

Whilst Ghana’s laboratory scientists are consummate professionals doing their best in trying circumstances, there is a limit to what their ingenuity only can achieve.

4. The government’s “aggressive tracing regime” has been faltering

Given all these supply chains and resource constraints, it is not too surprising then that the “aggressive tracing” promised as a partial substitute for the lockdowns appears to be slackening.

In the first week following the lockdown, tracing surged from 635 contacts reached to 5308. The sample collection rose from 589 to 4969. By 19^th April, the day the lockdown was lifted, contact tracing figures were down to 2049 and samples collected were as low as 1018. Considering that infection stats are highly sensitive to overall levels of tracing and sample collection, this apparent slackening is worrying.

5. The interpretations being given by the government about the ratio of positive cases to overall tested results are statistically loose

At the time of the lifting of the lockdown, much was made of the fact that only 1042 out of 68,591 (ergo, 1.52%) tested subjects were positive. However, that analysis involves a bit of mixing apples and oranges. Some of the testing protocols are so different in their quality that their results should not be allowed to dilute the overall picture.

To illustrate this point, I shall focus solely on the central hotspot of the epidemic in Ghana, Greater Accra. I was lucky enough to get a hold of the Greater Accra Covid-19 situational report of 21^st April 2020, just around the period the lockdown was lifted.

The high-level breakdown of the aggregated numbers in Greater Accra as at that date was as follows:

In Accra, people who are being referred for testing because they are showing Covid-19 symptoms (i.e. through “routine surveillance”), at the time the lockdown was lifted, had an 8.5% chance of testing positive. People who were identified for testing because they had come close to someone confirmed as infected had an 8% chance of being positive too.

These two categories of people are being targeted for testing using very established and grounded epidemiological methods. The people who are being randomly tested based on the GHS model of which communities are at risk tend to have a much lower probability of testing positive. The interpretation the government’s advisors have given to this fact is that community spread is low. The more likely answer is that the GHS model is weak. Since they refuse to publish and defend it before independent analysts, most biostatisticians I have discussed this issue with dismisses the model out of hand.

6. Even the higher ratio of positive cases in routine surveillance may be underestimating true spread

Ghana’s routine surveillance programs have considerable weaknesses. In 2010/2011, and again in 2017, they failed to detect H1N1 outbreaks till very late. In the 2017 episode, four KUMACA students who died of H1N1 at the Komfo Anokye Teaching Hospital were diagnosed only after death. According to the present Auditor-General, the Veterinary Services Department failed or neglected to set up an Asian Influenza pandemic preparedness system in 2010, opting instead to devote the money to workshops. When the epizootic crisis hit, over 400,000 livestock belonging to poor Ghanaian farmers perished from H1N1. The 8.5% positive testing rate recorded in Accra for Covid-19 suspected cases at the time of lifting the lockdown may thus have been lower than the true situation.

I do note that, in more recent days, the national-level routine surveillance ratio of positive cases has fallen to as low as 2.5%. Since situation reports for other regions are hard to come by, it is unclear if suspicion parameters and case definitions are identical nationwide. For instance, when composing the national picture, the GHS, unlike the regional directorates, lumps the community screening activities (based on its proprietary model) with the enhanced contact tracing activities thereby obscuring the fact that it is virtually not detecting any cases through the so-called “community screening” exercises, most likely due to weak modelling.

7. There is concrete evidence that the GHS risk-based model for community screening is weak

Using this proprietary model, the government decided to concentrate its efforts in Ayawaso West. At one point, it was even suggested that screening in this district will be universal and compulsory, only for the proclamation to be withdrawn later without explanation or ceremony.

It soon became clear that the transmission dynamics were far more complex. In a few days, the virus penetrated deeply into Ayawaso Central, an extremely high-density, inner-city enclave of the city, where suburbs such as Nima, Maamobi and Kanda are clustered. Then it stormed Accra Central (Jamestown, the High Street, the Central Business District, etc) before making the most fascinating move of all: turning Korle Klottey (Osu, Ridge, North Adabraka, Odorna, etc) into the fastest growing hotspot.

A simple biostatistical model based on covariance analysis of how trends in positive case confirmations across economically connected zones align should have shown clearly that commuter patterns of informal labour pools crisscrossing the Maamobi, Tudu, CBD, Odorna and North Adabraka inner-city rings were the primary features of interest. Some serious urbanography, not just epidemiology, should have been deployed immediately. The lack of open data prevented urban researchers from joining the fray.

8. Urbanographic analysis is clearly critical in anticipating the worst

Ayawaso East, Ayawaso West and Korle Klottey are the areas where COVID-19 related hospitalizations are likely to increase due to the growth trend of routine surveillance results. Accra Central and Ayawaso Central appear to be harbouring fast-growing numbers of asymptomatic individuals. How the trends will move from here on require an urbanographic, not just epidemiological, lens. COVID-19 always seems containable until it finds a vulnerable population or highly susceptible community in some cluster and then starts wreaking havoc. We can only hope that we can race ahead of the virus to identify such communities and ring-fence them before COVID-19 does.

9. The government’s attempt to push the narrative that it was investing heavily into testing resources created confusion about testing capacity

It turns out that it was the clever scientists at Noguchi that had found a workaround: pooled sampling, not massive injections of resources into testing infrastructure as the country was being told. Pooled sampling refers to the consolidation of multiple samples from different individuals for a single thermocycling run (i.e. single test).

10. Pooled sampling has rescued the country but it has important limits

India is one of the few countries in the world to have commissioned a detailed efficacy and ethical review of whether to update the national protocol on testing by allowing pooled samples. It did so just a little over a week ago but added many caveats, which should be of concern to Ghana too.

The India Council of Medical Research’s (ICMR’s) decision to impose a cap of five samples, a threshold Ghana initially adopted before “escalating” to 10 samples per well, speaks to the fear of overestimating diagnostic sensitivity thresholds. They also went further to permit pooled sampling only if the pre-test probability of positivity is lower than 2%.

Stanford’s Benjamin Pinsky, a clinical virologist, recently led a team to conduct mass community screening for Covid-19 (especially at sub-clinical level) in San Francisco. His team determined that a pre-test probability of 1% is the reasonable threshold to allow pooled sampling. These precautions put in place in other epidemiological contexts raise important issues for Ghana’s continued use of pooled sampling.

Firstly, a pooled sampling protocol is highly responsive to the specifics of the test kit in use, the epidemiological background, and the goals of screening. Hence, the protocols must be submitted to peer review and national-level ethical clearance, as has been done in India.

The ethical issues are compounded because differential diagnosis remains the standard of care in a context like Covid-19 where observed symptoms can be highly non-specific. Many respiratory pathogens could be implicated in the clinical presentation. (Even some health workers have taken to calling SARS-COV-2, the microbe that causes Covid-19, a “flu virus”, but it belongs to a completely different family of viruses).  In that regard, re-sampling for further tests could be warranted even if a negative result ensues. In a pooled testing scenario, this situation is complicated, especially in the absence of patient consent.

In light of this, only the mass screening exercises appear ethically suited for mass sampling. Routine surveillance and enhanced contacts tracing cases, with their high pre-test positivity rates, on the other hand, are best not confirmed through pooled sampling.

11. The issue of whether the denominator used in determining the positive case ratio is being overestimated remains unresolved

The different testing labs for Covid-19 in Ghana at present maintain separate indexes and case investigation form-coding procedures. There is currently no efficient way to harmonize and consolidate multiple tests performed on different samples from the same individual, especially also as the case investigation forms map to a unique sample ID but not to a unique patient ID. Multiple cases submitted to different labs would automatically count as separate cases.

Multiple cases submitted to the same lab can be harmonized against a single patient if the data is de-identified. In a pooled sampling regime, it is problematic to de-identify the samples constituting every pool to check for history without defeating the original goal of saving time.

These issues need to be clarified properly in an open and transparent manner before the 1.45% total positivity rate reported by the GHS on 22^nd April 2020 can be accepted at face value.

12. Open Data is NOT the enemy

Open Data is clearly not the enemy here. If anything it is the scorned friend waiting on the sidelines to save the day.

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