Overview of concept and location. (A) Schematic representation of underrepresentation and biased representation of a population. Circles represent individuals; black, blue, and green colors each represent one of three possible values for a demographic characteristic. Orange shaded areas represent individuals selected for inclusion in each data set. (B) Top: Inset map of the continent, Namibia in black and the region of interest outlined in red. Bottom: Detailed map of the area of interest showing population density in 2016 (yellow = high; blue = low; white = NA (Etosha National Park, no human population)). Study area circled in orange, health clinics (orange crosses), regional hospital (red cross). Map created in ArcGIS with data from GADM (https://gadm.org/). Credit: PLOS Digital Health (2023). DOI: 10.1371/journal.pdig.0000270
Mobile phone data is increasingly used in public health management and in responding to disease outbreaks, as demonstrated during the COVID-19 pandemic, when location data was used as a proxy for human movement and contacts and apps for informed exposure notifications. However, a new study led by Penn State researchers revealed that phone data may not accurately reflect underserved or particularly vulnerable populations, which are often also underrepresented in other data.
If this bias is not acknowledged or supplemented with additional data, the researchers said, reliance on telephone data in public health efforts could increase health inequality. They published their findings today (July 6). PLOS Digital Health.
“Populations with limited access to health care are also often overlooked in other data sources, including censuses,” said Nita Bharti, an associate professor of biology at Penn State Eberly College of Science and leader of the research team. “New, convenient data sources like cell phones can provide important insights into these populations, but it’s critical that we identify and measure their biases.”
According to Bharti, data gaps exist in all contexts and are easily seen in small rural populations. In this study, the researchers examined phone ownership, mobility and access to healthcare in a mobile rural population in Namibia as a case study to measure the representativeness of mobile phone data in populations regularly exposed to vaccine-preventable infectious diseases.
Namibia is a middle-income country in southern Africa, and Bharti said mobile phone data from the region is being used to guide public health decisions around malaria and other infectious diseases. Most Namibians live in urban areas with reliable access to healthcare, but this is not the case for rural or remote populations. The research team conducted detailed surveys of more than 250 people in two settlements in a remote area of Kunene province. The inhabitants are largely nomadic, migrating seasonally to herd livestock and the distance to the nearest health clinic is considerable.
![While cell phone data is increasingly used in public health management, a new study led by Penn State researchers reveals that phone data underrepresents vulnerable populations and that failure to account for biases in phone data may increase health inequality. The research team studied a rural mobile population in Namibia, pictured here, where access to health care is limited, as a case study to measure how well mobile phone data represents populations. Credit: Bharti Lab/Penn State Cell phone data used for public health does not represent vulnerable populations, new research finds](https://scx1.b-cdn.net/csz/news/800a/2023/mobile-phone-data-used-1.jpg)
While cell phone data is increasingly used in public health management, a new study led by Penn State researchers reveals that phone data underrepresents vulnerable populations and that failure to account for biases in phone data may increase health inequality. The research team studied a rural mobile population in Namibia, pictured here, where access to health care is limited, as a case study to measure how well mobile phone data represents populations. Credit: Bharti Lab/Penn State
The researchers found that phone ownership was relatively low: only 31% of participants owned a phone — compared to the estimated 95% in urban areas of the country in 2013 — and only 59% had ever used a phone. Phone owners and users were much more likely to be male, traveled to more locations, and had better access to health care.
“We found that, within these already vulnerable populations, the most vulnerable people were underrepresented in this phone data because they didn’t own phones or didn’t have access to phones,” said Alexandre Blake, a graduate student in Bharti’s lab at Penn State and first author of the paper.
“A common way to catch up on missing data is to simply scale it up and assume that missing data is the same as logged data. But we clearly found that the people who are missing in phone data are less mobile and have less access to health care. And in terms of making public health decisions, these are very important differences.”
According to Blake, mobile phones also created a distorted view of mobility among phone owners. Because phone owners often traveled to areas without phone reception, many of their movements would not be recorded in phone records.
“Even if you have a phone, you can only be tracked in locations where you receive a signal,” said Blake. “So phone data, especially from remote areas, will only capture a specific segment of the population and will only be able to record part of their movements. If phone data were used to predict the possible spread of an infectious disease in a region like the one we studied, it would most movements and contacts are missed. Without accounting for data bias, movements based on phone data would be misleading and ineffective for efforts to respond to outbreaks to limit the spatial spread of a disease.”
Because cell phone data may not accurately reflect the populations and locations most in need of public health improvements, the researchers suggested that relying on this data to inform public health decisions may actually be detrimental. and potentially exacerbate health inequalities. They stressed the importance of acknowledging and measuring biases in all types of data – not just public health ones – and using multiple types of data with non-overlapping biases when drawing conclusions.
“All data has biases but is still valuable resources, and phone data is no exception,” Bharti said. “Recognizing that data is not just under-representative and showing that it is in fact biased helps our field interpret data correctly, measure biases, and look for ways to measure what’s missing.”
According to the researchers, small, remote populations play an important but often overlooked role in the transmission and persistence of infectious diseases. Limited access to healthcare can lead to delayed detection of outbreaks, and overlooking these groups can delay the elimination of vaccine-preventable transmissible pathogens.
“Equal access to health care is a fundamental human right, and addressing health inequalities in underrepresented populations is essential for public health progress,” said Bharti.
“You don’t have to look at low- or middle-income countries to find underrepresentation in vulnerable groups. We would see the same absence of vulnerable groups in commonly used data if we looked, for example, at a rural part of Pennsylvania or Mississippi or in urban areas, such as New York City or Los Angeles There are gaps and biases in all data that underrepresent the segments of the population most in need of improved health services Failing to acknowledge these biases could divert resources away from these groups and lead of public health interventions that increase inequality.”
In addition to Bharti and Blake, the research team includes Ashley Hazel of the University of California, San Francisco, and John Jakurama and Justy Matundu of the Kaoko Information Center in Namibia.
More information:
Alexandre Blake et al, Disparities in mobile phone ownership reflect disparities in access to health care, PLOS Digital Health (2023). DOI: 10.1371/journal.pdig.0000270
Quote: Data from mobile phones used for public health underrepresent vulnerable populations, finds new study (2023, July 6) retrieved July 7, 2023 from https://medicalxpress.com/news/2023-07-mobile-health-underrepresent- vulnerable populations. html
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