When cases aren’t counted – why measuring burden of disease matters

What is the burden of disease?

In a world of numerous diseases and health conditions, not every disease is measured the same, or even counted at all. So how do we determine which ones have the most impact, or burden, on a population? While measuring “burden of disease” is a commonly used term, there is no universally accepted definition. This is because it depends on what disease is being described, who is asking, and the context in which it’s being talked about.

The burden of disease is a way to measure and evaluate the human and economic costs that result from poor health, in a specific location.

Understanding the burden and epidemiology of diseases is critical for demonstrating the need for interventions, deciding how to best allocate limited resources, and improve disease outcomes. It is a way to evaluate the relative importance of a specific disease. While public health might be interested in the ways the burden of disease costs the healthcare system, an organization invested in building therapeutics or solutions might consider its impact differently. And for certain types of diseases – such as infectious diseases – understanding its burden is critical when considering how to best prevent diseases from spreading.

So how do we measure the burden of disease?

This may seem like a daunting task – even defining “burden” and “disease” can be subjective, depending on what health resources are available, the nature of the disease, or what data sources are reporting the disease metrics. Epidemiologists often categorize “disease” by sub-categories, such as injuries, non-communicable diseases, and infectious diseases. Different diseases contribute differently towards this collective burden which requires different types of surveillance methods.

There are also two common ways to measure burden – the “biomedical” approach which measures the impact of disease-related sickness or disabilities on humans. This is often measured through surveillance measures that are attributable to the disease, like the ones we can count. These might include:

  • Deaths resulting from the disease
  • Disease cases (either new or the baseline number of cases in a population)
  • Healthcare visits (e.g., ER visits, hospitalizations, outpatient visits)
  • Life expectancy in years of an individual affected by a disease

However, these measures don’t necessarily show the full picture. The measures that are less straightforward or difficult to track are just as important in describing the burden of disease – particularly if they are underreported, changing quickly, or are never-seen-before. These might include:

  • Trends that describe the patterns of disease cases among populations over time
  • Risk of becoming ill (i.e., how much of a risk is disease X?)
  • For diseases that spread, population features such as density, mixing behaviours, and travel patterns

Another way to think about measuring burden of disease is from a socioeconomic perspective, which involves costs associated with the disease, or calculations of “healthy years of life lost” to compare the burden of different diseases. These can include:

  • Direct costs – expenditures on treatment, prevention, testing, therapeutics, healthcare visits, and other interventions for the disease.
  • Indirect costs – the loss of income or economic output due to disease-related disruptions.
  • Health-adjusted life years (HALYs) – often calculated as quality-adjusted life years (QALYs) and disability-adjusted life years (QALYs), that assign weighted values associated with states of health or disease towards life expectancy. This can allow for comparisons between different diseases, populations, and interventions.

The burden of disease involves all the costly implications around the diseases, as well as for the environments and societies in which they exist.

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Why is measuring burden of disease so challenging?

Measuring disease burden requires consistent, robust reporting on diseases – which requires ongoing investment in healthcare resources and infrastructure, strong public access to healthcare, and the sociopolitical capacity for reporting transparency.

A major challenge with measuring disease burden relies on a regional capacity for disease surveillance. These factors make disease reporting highly inconsistent across the globe, since not every cost can be measured, case data are often imperfect and reported inconsistently, and healthcare inequities mean that certain diseases do not affect everyone equally, especially for developing countries and emerging markets.

While certain disease categories such as non-communicable diseases are consistently predictable within populations, and do not change rapidly, not every disease is created equal.

Let’s take infectious diseases as an example. Measuring the disease burden of infectious diseases poses a unique challenge since they have the potential to spread quickly and can be novel or unknown – which contributes to underdiagnosis and underreporting. The capacity for infectious disease surveillance is highly also dependent on the resources available to capture disease activity. As a result, infectious diseases pose a greater risk for disruption and are harder to quantify at the same time.

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What are some ways we can overcome these challenges?

Modern advances in artificial intelligence (AI) and data science have improved the ability to adjust for gaps in traditional methods of reporting and assess for the true burden of disease, happening now, tomorrow, and in the long term. These technological advances in event-based surveillance enable users of disease burden data – across both the public and private sectors – to overcome systemic underreporting from traditional methods of reporting, while staying on top of the diseases trends that rapidly shift or evolve. Paying attention to the ways in which measures of disease burden shift for a defined region helps inform, balance decisions, and create frameworks in better understanding how it impacts a unique population.

With a more holistic, yet granular, picture of disease activity today, disease burden models are based on a more representative and accurate base of the true scale of individuals impacted by a disease. This results in a clearer picture of how underreported diseases impact a population or area over time, as an innovative way to overcome the long-standing challenges around disease surveillance and healthcare infrastructure.

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