Disease map with glowing hotspots and statistical data

Mapping Disease: Why The Details Matter for Spotting Health Trends

Lena Voss in Health & Wellness December 2025 • 4 min read.

"Uncover how different methods of calculating expected cases impact disease maps and spatio-temporal analysis. See why accurate mapping is vital for public health."

Disease maps are powerful tools. They visually represent the spread and patterns of illnesses. These maps help us understand where diseases hit hardest and how they're changing. But how we create these maps matters—a lot. The choices we make in calculating the data can significantly change what the map shows us.

One key step is figuring out the 'expected cases' – how many cases we'd anticipate in a certain area based on general rates. This number becomes our yardstick for measuring if a region has more or fewer cases than expected. However, there's more than one way to calculate those expected cases. Do we use the average rates for the whole study period? Or do we use specific rates for each smaller chunk of time within the study?

Recent research digs into this very question. The researchers looked at stomach cancer mortality in Spain over two decades. They compared different methods of calculating expected cases and mapped the results. Their findings highlight how much these choices matter. Depending on the method, the maps could reveal different patterns and trends. This article breaks down their findings, showing you why choosing the right method is crucial for accurate disease mapping.

Different Roads to the Same Destination? How Calculation Methods Change the Map

Disease map with glowing hotspots and statistical data

Researchers in the study focused on municipal-level data of stomach cancer mortality in men in Spain from 1989 to 2008. They wanted to see how different calculation methods affected the resulting disease maps. They calculated "expected cases" in two main ways:

Using reference rates for each 5-year period: This means they used mortality rates specific to each five-year chunk of the study (1989-1993, 1994-1998, 1999-2003, and 2004-2008).

Using average reference rates for the overall period: Here, they calculated one average mortality rate for the entire 1989-2008 timespan.

They then used these different sets of expected cases to generate maps using spatial and spatio-temporal models – basically, different ways of statistically smoothing the data to reveal underlying patterns. What they discovered was eye-opening: the choice of calculation method significantly changed the maps.

The Takeaway: Why This Matters for Public Health

This research has important implications for anyone involved in public health and disease mapping. It demonstrates that creating disease maps isn't just about plugging in numbers. The choices we make in how we calculate those numbers—especially expected cases—can dramatically affect what the maps reveal.

So, what's the right approach? The answer depends on the goals. If you're trying to spot time trends in specific areas, using average reference rates might be the way to go. But, if you're more interested in seeing how the spatial pattern of a disease is changing, using local rates for each period is a better bet.

Ultimately, being aware of these nuances can lead to more accurate and insightful disease maps. And those maps can help us target resources, develop effective interventions, and improve public health outcomes.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.4081/gh.2014.3, Alternate LINK

Title: Disease Mapping And Spatio-Temporal Analysis: Importance Of Expected-Case Computation Criteria

Subject: Health Policy

Journal: Geospatial health

Publisher: PAGEPress Publications

Authors: Gonzalo López-Abente, Nuria Aragonés, Javier García-Pérez, Pablo Fernández- Navarro

Published: 2014-11-01

Everything You Need To Know

What are disease maps and why are they important?

Disease maps are visual tools used to represent the spread and patterns of illnesses. These maps are important because they help public health officials understand where diseases are most prevalent and how they change over time. This understanding is critical for identifying health trends, allocating resources, and implementing effective public health interventions. The accuracy of these maps relies heavily on the methods used to calculate the data, specifically the 'expected cases'.

How does the calculation of 'expected cases' work, and why does it matter?

Calculating 'expected cases' involves determining how many disease cases are anticipated in a specific area, based on general rates. The choice of how to calculate 'expected cases' can significantly impact the patterns displayed on disease maps. For instance, the use of average reference rates for the overall period versus specific rates for smaller time periods, like five-year intervals, can lead to different visual representations of the disease's prevalence. This choice affects the ability to accurately identify and understand health trends.

What specific methods did the researchers use in their study?

Researchers in the study examined stomach cancer mortality in men in Spain from 1989 to 2008. They compared two primary methods for calculating 'expected cases': using reference rates for each five-year period and using average reference rates for the overall period. They then generated maps using spatial and spatio-temporal models. The results revealed significant differences in the resulting maps depending on which method was used to calculate 'expected cases', highlighting the importance of methodological choices.

What are the key implications of the choice of calculation method?

The research shows that the method used to calculate 'expected cases' greatly influences the patterns observed on disease maps. Utilizing specific rates for shorter periods can reveal localized trends and changes that might be missed when using a single average rate. For example, if specific five-year periods are used, this method can reveal underlying patterns. If the average reference rates are used the maps may not be as accurate. This affects the identification of trends, potentially leading to misinterpretations of disease prevalence and spread, thereby impacting public health decisions.

Why is this research significant for public health?

The implications of this research are significant for anyone involved in public health and disease mapping. It underscores that disease map creation is not simply a data-entry process. Careful consideration of the calculation methods, especially for 'expected cases', is vital. Choosing the right method is crucial for the maps to accurately reflect the reality on the ground. This accuracy directly impacts the ability of public health professionals to detect and understand patterns and trends, which is essential for effective resource allocation and intervention strategies.