Allegheny Duo Tackles Data Problems in African Health Surveys With Online Tool

A new study published in 2025 is shining a light on a major issue in global health- at the core of the research team were two members of the Allegheny College community.

Amelia Finaret, associate professor of global health studies, and Abigail W. Smith '22 joined a group of international researchers to uncover just how different the quality of health data is across sub-Saharan Africa.

The problem? Inconsistent survey quality that could have significant consequences for health and development programs across the continent.

The project, which began four years ago, looked at the large household surveys used by governments and international organizations to track everything from child health to poverty rates.

These surveys, such as the Demographic and Health Surveys (DHS), guide critical decisions about aid, policy, and healthcare resources. But as the research team found, not all the data is created equal.

"We found that the quality of survey data really varies depending on where it's collected," Finaret says. "Rural and remote areas tend to have more errors or missing information, and unfortunately, those are often the places that need help the most."

Smith joined the project while still a student at Allegheny, double majoring in environmental science and sustainabilityand global health studies, digging into the literature and tracking down other studies on data quality.

She recently completed her master's degree in public health at the University of Pittsburgh, adding, "Getting the chance to work on this research as an undergraduate was life-changing for me. It really sparked my passion for research and data quality. Prior to completing this work I had never thought much about data and who is and is not being included. Now this is something I think about on a daily basis.

"She found so much useful background information," Finaret recalls. "It was super impressive and made a big difference in how we approached things."

Finaret, who officially joined the international team in 2021, played a major role in writing the report and advising on the statistical models. Coordinating across time zones with researchers in the United Kingdom and Austria wasn't always easy, she says, but the team dynamic was strong. "Everyone brought something to the table," she adds.

So what kind of problems did they find in the data? A lot of it came down to human error.

In some areas; people might not remember their exact age, or they might round it off. Children's heights and ages sometimes got recorded incorrectly. These may seem like small slip-ups, but they can seriously distort the bigger picture, especially when it comes to measuring malnutrition or disease.

Using high-tech mapping tools, the researchers were able to visualize exactly where the data quality drops off, and not surprisingly, it's usually far from city centers or areas with bright lights and easy access.

"One possibility is that survey workers, called enumerators, have a tough time staying overnight in remote regions," Finaret says. "They may rush through the process or skip visits altogether."

She says she'd like to dig deeper into the role of these field workers, who are often overlooked but play an essential role in collecting the very data that drives global health decisions.

"They're absolutely essential," Finaret notes. "We need to understand their challenges better if we want to improve data quality."

One of the major takeaways the team found is that authorities can't always trust big surveys just because they have large sample sizes. Errors and inconsistencies can be very localized, so if authorities are designing a program for a specific region, they might be basing it on shaky data.

To help fix that, the team created an interactive online tool that shows where survey data is most and least reliable. It tracks things like unusual age reporting patterns and suspect height measurements. The idea is to help policymakers and field teams target their efforts and training where it's needed most.

"People often assume data is just … there and correct," Finaret says. "But that's not always the case, and it's important to know what you're working with before making big decisions."

With global health agencies such as USAID facing major funding cuts, the issue is more urgent than ever. "Without solid data, we won't know where people are missing vaccines, getting malaria, or facing food insecurity," Finaret warns. "We're losing the information needed to save lives. It's devastating."