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We Need Better NCD Data for COVID-19 Response in LMICs

By Guest Writer on September 10, 2020

digital health NCD report

As the old adage goes, you can’t improve what you don’t measure. When it comes to containing the COVID-19 pandemic in the world’s poorest countries, there is a lot – too much – that we do not measure, especially related to non-communicable diseases (NCDs).

We have known for years that obesity and diabetes increase the chance of diseases such as cancer, heart disease, stroke, dementia and Alzheimer’s. In the last few months, we have learned that underlying conditions such as obesity, diabetes, and other chronic conditions also put patients at higher risk for severe COVID-19 outcomes, such as hospitalization and death.

To date, national and high-quality data on these factors are by and large missing in low-and middle-income countries (LMICs). Given the now-proven devastating compounding effect of chronic conditions and COVID-19, in terms of both health and economic consequences, it is critical that we address this issue.

The need for NCD data

Having data on the prevalence of NCDs within a country – at the regional and local levels, particularly – is important for many reasons.

By using it to identify high-risk communities, decision-makers can allocate life-saving resources more effectively. This is ever more important as we work to develop a safe and effective vaccine, but it’s also critical now, as resources like ventilators can be proactively sent to hospitals in areas with higher NCD prevalence.

Estimates of infection hospitalization rates and projected infection fatality rates, which help us plan and prepare, also rely on having good estimates of NCDs at the most granular, local level.

And knowing where pockets of high prevalence NCDs are can help us proactively manage risk in those areas and prepare health care systems accordingly, since there will be more COVID-19 cases that require hospitalization.

Why existing NCD data are insufficient

In some cases, COVID-19 itself is responsible for halting general data collection for NCDs. For example, in Nigeria, the NCD STEPS survey, a World Health Organization-recommended framework for NCD surveillance, was supposed to be completed in 2020. However, it is currently on hold due to the pandemic.

There are further limitations with NCD data. Though it may be freely accessible, it is often not updated in real time and not localized. Surgo Foundation researchers experienced this problem recently when building a COVID-19 vulnerability index for Africa: data about NCDs below national levels was extremely hard to find. Some African countries had regional data, but others didn’t – and no one consistently measured the same diseases.

Also, existing NCD data are often antiquated. For example, because hypertension is a relatively well-understood disease, much of the prevalence and treatment data around hypertension globally is outdated – in fact, it is often over 10 years old. As NCD prevalence continues to rise, these data points are no longer a valid estimate of the NCD burden in a country.

What’s worse, existing NCD data are often not broken down into the categories people need. For example, while the WHO Noncommunicable Diseases Country Profiles separate risk factor prevalence by gender, they do not separate into specific age groups, which provides a more complete picture.

Why better NCD data matters

As time passes, more possible treatments emerge for COVID-19. While dexamethasone, for example, is cheap, it is possible that other treatments may be more expensive, limiting the amount governments can procure. With limited treatments, knowing which people are most vulnerable to severe COVID-19 outcomes makes it easier to properly distribute them.

Similarly, when a vaccine first comes to market, there will not be enough doses for everyone. By properly understanding which communities have high chronic disease burdens and in turn are at high risk for COVID-19, vaccines can be distributed to priority areas with the aim of saving more lives.

Additionally, while a majority of WHO Member States include addressing chronic diseases within their COVID-19 response plans, many have also reported a significant financing gap implementing measures for NCD treatment and care: recent data from the WHO show that more than 50% of countries reported disruption in normal care for some chronic conditions.

With better data, health system leaders can prioritize where we most need to implement these measures, where to send medical professionals to deliver care, where to not pull staff from for COVID-19, and where budget must be allocated to normal delivery of care.

5 steps we can take now

There are steps we can take right away to address the limitations of the currently available data on NCDs:

1. Harness technology.

The widespread use of cell phones, wearable technologies, electronic medical records, and other forms of technology have helped increase the amount of health data being collected. These data measuring tools are readily available (especially in high income countries), yet the data they could capture is not yet harnessed for public good. A huge gap in the market exists for implementing these technologies to measure the health outcomes of people in LMICs.

2. Use stand-in data.

LMICs that are missing certain data on NCDs can use stand-in data from countries with similar GDPs and health infrastructure. And for local data, much of what does exist is split into rural versus urban. Therefore, rural communities missing local data can use data from other rural communities, and urban areas can pull from studies in urban environments.

3. Promote data sharing.

Different groups collect different data points. By collaborating and creating strategic partnerships, various groups can increase and diversify available data. These partnerships can be leveraged to create more sustainable data collecting systems in the future.

4. Adjust data to new trends

Find outdated data or more recent global data or data from other areas to determine a trend in the data points. Apply this pattern to estimate current figures from older records.

5. Combine multiple data sources

Data connecting many behavioral and environmental factors to NCDs are not widely available. Until this is collected, find the necessary data from multiple data sources and find a correlation if possible.

We desperately need better NCD data in order to make better projections and guide policy decisions as the pandemic continues to burn across the globe – otherwise our best forecast models for COVID-19 are grounded in weak estimates. Let’s act now to ensure all countries are armed with the information they need.

By Andrea Feigl, PhD and Sema Sgaier, PhD,

Filed Under: Healthcare
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One Comment to “We Need Better NCD Data for COVID-19 Response in LMICs”

  1. This is a spot-on blog and particularly helpful to offer do-able recommendations. It’s correct to link poor COVID-19 capabilities to poor NCD surveillance. That’s why linked-up surveillance is so important.