Dec. 29 (UPI) — Scientists at the Amsterdam Institute for Global Health and Development have developed a new model that uses economic data to predict antimicrobial resistance levels across different countries.
To calculate antibiotic resistance within a given country, the model — described this week in the journal PNAS — considers a country’s average income, the average out-of-pocket health care costs of its citizens and the degree of government corruption found within a country’s borders.
Over the last decade, common antibiotics have become less effective at combating bacterial infections.
The problem has been antimicrobial resistance has been exacerbated by the overprescription of antibiotics by doctors, as well as by the overuse of antibiotics in the commercial livestock industry.
In most developed nations, regulators have worked to track the problem and institute reforms to address the issues of overprescription and overuse.
While doctors, in many countries, are required to report the bacterial infections they treat and the antibiotic therapies they prescribe, the problem is less well-documented in developing nations.
“Substantial clinical surveillance gaps exist in low- and middle-income countries,” the authors report in their paper.
In many of these countries, there isn’t the funding or infrastructure needed for detailed tracking efforts.
To improve antibiotic resistant surveillance in developing countries, researchers decided to look at the relationship between socioeconomic factors and the prevalence of nine common pathogens noted for their resistance to antibiotics.
The researchers then worked backwards to predict the prevalence of antibiotic-resistant bacteria in countries where surveillance data is lacking.
When scientists tested their model’s predictions against countries where antimicrobial resistance is effectively tracked, they found it was 78 to 86 percent accurate for six of the nine pathogens.
The new model could be used to identify places where efforts to track antimicrobial resistance need to be strengthened.
“Our global maps of national resistance prevalence provide data-driven criteria for the prioritization of surveillance efforts in [low- and middle-income countries],” researchers wrote. “First, surveillance might be prioritized in countries where high resistance rates are expected.”
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